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10 Current Database Research Topic Ideas in 2024

Home Blog Database 10 Current Database Research Topic Ideas in 2024

As we head towards the second half of 2024, the world of technology evolves at a rapid pace. With the rise of AI and blockchain, the demand for data, its management and the need for security increases rapidly. A logical consequence of these changes is the way fields like database security research topics and DBMS research have come up as the need of the hour.

With new technologies and techniques emerging day-by-day, staying up-to-date with the latest trends in database research topics is crucial. Whether you are a student, researcher, or industry professional, we recommend taking our Database Certification courses to stay current with the latest research topics in DBMS.

In this blog post, we will introduce you to 10 current database research topic ideas that are likely to be at the forefront of the field in 2024. From blockchain-based database systems to real-time data processing with in-memory databases, these topics offer a glimpse into the exciting future of database research.

So, get ready to dive into the exciting world of databases and discover the latest developments in database research topics of 2024!

Blurring the Lines between Blockchains and Database Systems 

The intersection of blockchain technology and database systems offers fertile new grounds to anyone interested in database research.

As blockchain gains popularity, many thesis topics in DBMS[1] are exploring ways to integrate both fields. This research will yield innovative solutions for data management. Here are 3 ways in which these two technologies are being combined to create powerful new solutions:

Immutable Databases: By leveraging blockchain technology, it’s possible to create databases to be immutable. Once data has been added to such a database, it cannot be modified or deleted. This is particularly useful in situations where data integrity is critical, such as in financial transactions or supply chain management.

Decentralized Databases: Blockchain technology enables the creation of decentralized databases. Here data is stored on a distributed network of computers rather than in a central location. This can help to improve data security and reduce the risk of data loss or corruption.

Smart Contracts: Smart contracts are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. By leveraging blockchain technology, it is possible to create smart contracts that are stored and executed on a decentralized database, making it possible to automate a wide range of business processes.

Childhood Obesity: Data Management 

Childhood obesity is a growing public health concern, with rates of obesity among children and adolescents rising around the world. To address this issue, it’s crucial to have access to comprehensive data on childhood obesity. Analyzing information on prevalence, risk factors, and interventions is a popular research topic in DBMS these days.

Effective data management is essential for ensuring that this information is collected, stored, and analyzed in a way that is useful and actionable. This is one of the hottest DBMS research paper topics. In this section, we will explore the topic of childhood obesity data management.

A key challenge to childhood obesity data management is ensuring data consistency. This is difficult as various organizations have varied methods for measuring and defining obesity. For example:

Some may use body mass index (BMI) as a measure of obesity.

Others may use waist circumference or skinfold thickness.   Another challenge is ensuring data security and preventing unauthorized access. To protect the privacy and confidentiality of individuals, it is important to ensure appropriate safeguards are in place. This calls for database security research and appropriate application.

Application of Computer Database Technology in Marketing

Leveraging data and analytics allows businesses to gain a competitive advantage in this digitized world today. With the rising demand for data, the use of computer databases in marketing has gained prominence.

The application of database capabilities in marketing has really come into its own as one of the most popular and latest research topics in DBMS[2]. In this section, we will explore how computer database technology is being applied in marketing, and the benefits this research can offer.

Customer Segmentation: Storage and analysis of customer data makes it possible to gain valuable insights. It allows businesses to identify trends in customer behavior, preferences and demographics. This information can be utilized to create highly targeted customer segments. This is how businesses can tailor their marketing efforts to specific groups of customers.

Personalization: Computer databases can be used to store and analyze customer data in real-time. In this way, businesses can personalize their marketing and offers based on individual customer preferences. This can help increase engagement and loyalty among customers, thereby driving greater revenue for businesses.

Predictive Analytics: Advanced analytics techniques such as machine learning and predictive modeling can throw light on patterns in customer behavior. This can even be used to predict their future actions. This information can be used to create more targeted marketing campaigns, and to identify opportunities for cross-selling and upselling.

Database Technology in Sports Competition Information Management

Database technology has revolutionized the way in which sports competition information is managed and analyzed. With the increasing popularity of sports around the world, there is a growing need for effective data management systems that can collect, store, and analyze large volumes of relevant data. Thus, researching database technologies[3] is vital to streamlining operations, improving decision-making, and enhancing the overall quality of events.

Sports organizations can use database technology to collect and manage a wide range of competition-related data such as: 

Athlete and team information,

competition schedules and results,

performance metrics, and

spectator feedback.

Collating this data in a distributed database lets sports organizations easily analyze and derive valuable insights. This is emerging as a key DBMS research paper topic.

Database Technology for the Analysis of Spatio-temporal Data

Spatio-temporal data refers to data which has a geographic as well as a temporal component. Meteorological readings, GPS data, and social media content are prime examples of this diverse field. This data can provide valuable insights into patterns and trends across space and time. However, its multidimensional nature makes analysis be super challenging. It’s no surprise that this has become a hot topic for distributed database research[4].

In this section, we will explore how database technology is being used to analyze spatio-temporal data, and the benefits this research offers.

Data Storage and Retrieval: Spatio-temporal data tends to be very high-volume. Advances in database technology are needed to make storage, retrieval and consumption of such information more efficient. A solution to this problem will make such data more available. It will then be easily retrievable and usable by a variety of data analytics tools.

Spatial Indexing: Database technology can create spatial indexes to enable faster queries on spatio-temporal data. This allows analysts to quickly retrieve data for specific geographic locations or areas of interest, and to analyze trends across these areas.

Temporal Querying: Distributed database research can also enable analysts to analyze data over specific time periods. This facilitates the identification of patterns over time. Ultimately, this enhances our understanding of how these patterns evolve over various seasons.

Artificial Intelligence and Database Technology

Artificial intelligence (AI) is another sphere of technology that’s just waiting to be explored. It hints at a wealth of breakthroughs which can change the entire world. It’s unsurprising that the combination of AI with database technology is such a hot topic for database research papers[5] in modern times. 

By using AI to analyze data, organizations can identify patterns and relationships that might not be apparent through traditional data analysis methods. In this section, we will explore some of the ways in which AI and database technology are being used together. We’ll also discuss the benefits that this amalgamation can offer.

Predictive Analytics: By analyzing large volumes of organizational and business data, AI can generate predictive models to forecast outcomes. For example, AI can go through customer data stored in a database and predict who is most likely to make a purchase in the near future.

Natural Language Processing: All businesses have huge, untapped wells of valuable information in the form of customer feedback and social media posts. These types of data sources are unstructured, meaning they don’t follow rigid parameters. By using natural language processing (NLP) techniques, AI can extract insights from this data. This helps organizations understand customer sentiment, preferences and needs.

Anomaly Detection: AI can be used to analyze large volumes of data to identify anomalies and outliers. Then, a second round of analysis can be done to pinpoint potential problems or opportunities. For example, AI can analyze sensor data from manufacturing equipment and detect when equipment is operating outside of normal parameters.

Data Collection and Management Techniques of a Qualitative Research Plan

Any qualitative research calls for the collection and management of empirical data. A crucial part of the research process, this step benefits from good database management techniques. Let’s explore some thesis topics in database management systems[6] to ensure the success of a qualitative research plan.

Interviews: This is one of the most common methods of data collection in qualitative research. Interviews can be conducted in person, over the phone, or through video conferencing. A standardized interview guide ensures the data collected is reliable and accurate. Relational databases, with their inherent structure, aid in this process. They are a way to enforce structure onto the interviews’ answers.

Focus Groups: Focus groups involve gathering a small group of people to discuss a particular topic. These generate rich data by allowing participants to share their views in a group setting. It is important to select participants who have knowledge or experience related to the research topic.

Observations: Observations involve observing and recording events in a given setting. These can be conducted openly or covertly, depending on the research objective and setting. To ensure that the data collected is accurate, it is important to develop a detailed observation protocol that outlines what behaviors or events to observe, how to record data, and how to handle ethical issues.

Database Technology in Video Surveillance System 

Video surveillance systems are used to monitor and secure public spaces, workplaces, even homes. With the increasing demand for such systems, it’s important to have an efficient and reliable way to store, manage and analyze the data generated. This is where database topics for research paper [7] come in.

By using database technology in video surveillance systems, it is possible to store and manage large amounts of video data efficiently. Database management systems (DBMS) can be used to organize video data in a way that is easily searchable and retrievable. This is particularly important in cases where video footage is needed as evidence in criminal investigations or court cases.

In addition to storage and management, database technology can also be used to analyze video data. For example, machine learning algorithms can be applied to video data to identify patterns and anomalies that may indicate suspicious activity. This can help law enforcement agencies and security personnel to identify and respond to potential threats more quickly and effectively.

Application of Java Technology in Dynamic Web Database Technology 

Java technology has proven its flexibility, scalability, and ease of use over the decades. This makes it widely used in the development of dynamic web database applications. In this section, we will explore research topics in DBMS[8] which seek to apply Java technology in databases.

Java Server Pages (JSP): JSP is a Java technology that is used to create dynamic web pages that can interact with databases. It allows developers to embed Java code within HTML scripts, thereby enabling dynamic web pages. These can interact with databases in real-time, and aid in data collection and maintenance.

Java Servlets: Java Servlets are Java classes used to extend the functionality of web servers. They provide a way to handle incoming requests from web browsers and generate dynamic content that can interact with databases.

Java Database Connectivity (JDBC): JDBC is a Java API that provides a standard interface for accessing databases. It allows Java applications to connect to databases. It can SQL queries to enhance, modify or control the backend database. This enables developers to create dynamic web applications.

Online Multi Module Educational Administration System Based on Time Difference Database Technology 

With the widespread adoption of remote learning post-COVID, online educational systems are gaining popularity at a rapid pace. A ubiquitous challenge these systems face is managing multiple modules across different time zones. This is one of the latest research topics in database management systems[9].

Time difference database technology is designed to handle time zone differences in online systems. By leveraging this, it’s possible to create a multi-module educational administration system that can handle users from different parts of the world, with different time zones.

This type of system can be especially useful for online universities or other educational institutions that have a global reach:

It makes it possible to schedule classes, assignments and other activities based on the user's time zone, ensuring that everyone can participate in real-time.

In addition to managing time zones, a time difference database system can also help manage student data, course materials, grades, and other important information.

Why is it Important to Study Databases?

Databases are the backbone of many modern technologies and applications, making it essential for professionals in various fields to understand how they work. Whether you're a software developer, data analyst or a business owner, understanding databases is critical to success in today's world. Here are a few reasons why it is important to study databases and more database topics for research paper should be published:

Efficient Data Management

Databases enable the efficient storage, organization, and retrieval of data. By studying databases, you can learn how to design and implement effective data management systems that can help organizations store, analyze, and use data efficiently.

Improved Decision-Making

Data is essential for making informed decisions, and databases provide a reliable source of data for analysis. By understanding databases, you can learn how to retrieve and analyze data to inform business decisions, identify trends, and gain insights.

Career Opportunities

In today's digital age, many career paths require knowledge of databases. By studying databases, you can open up new career opportunities in software development, data analysis, database administration and related fields.

Needless to say, studying databases is essential for anyone who deals with data. Whether you're looking to start a new career or enhance your existing skills, studying databases is a critical step towards success in today's data-driven world.

Final Takeaways

In conclusion, as you are interested in database technology, we hope this blog has given you some insights into the latest research topics in the field. From blockchain to AI, from sports to marketing, there are a plethora of exciting database topics for research papers that will shape the future of database technology.

As technology continues to evolve, it is essential to stay up-to-date with the latest trends in the field of databases. Our curated KnowledgeHut Database Certification Courses will help you stay ahead of the curve and develop new skills.

We hope this blog has inspired you to explore the exciting world of database research in 2024. Stay curious and keep learning!

Frequently Asked Questions (FAQs)

There are several examples of databases, with the five most common ones being:

MySQL : An open-source RDBMS used commonly in web applications.

Microsoft SQL Server : A popular RDBMS used in enterprise environments.

Oracle : A trusted commercial RDBMS famous for its high-scalability and security.

MongoDB : A NoSQL document-oriented database optimized for storing large amounts of unstructured data.

PostgreSQL : An open-source RDBMS offering advanced features like high concurrency and support for multiple data types.

Structured Query Language (SQL) is a high-level language designed to communicate with relational databases. It’s not a database in and of itself. Rather, it’s a language used to create, modify, and retrieve data from relational databases such as MySQL and Oracle.

A primary key is a column (or a set of columns) that uniquely identifies each row in a table. In technical terms, the primary key is a unique identifier of records. It’s used as a reference to establish relationships between various tables.

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Database Management Systems (DBMS)

Database group website: db.cs.berkeley.edu

Declarative languages and runtime systems

Design and implementation of declarative programming languages with applications to distributed systems, networking, machine learning, metadata management, and interactive visualization; design of query interface for applications.

Scalable data analysis and query processing

Scalable data processing in new settings, including interactive exploration, metadata management, cloud and serverless environments, and machine learning; query processing on compressed, semi-structured, and streaming data; query processing with additional constraints, including fairness, resource utilization, and cost.

Consistency, concurrency, coordination and reliability

Coordination avoidance, consistency and monotonicity analysis; transaction isolation levels and protocols; distributed analytics and data management, geo-replication; fault tolerance and fault injection.

Data storage and physical design

Hot and cold storage; immutable data structures; indexing and data skipping; versioning; new data types; implications of hardware evolution.

Metadata management

Data lineage and versioning; usage tracking and collective intelligence; scalability of metadata management services; metadata representations; reproducibility and debugging of data pipelines.

Systems for machine learning and model management

Distributed machine learning and graph analytics; physical and logical optimization of machine learning pipelines; online model management and maintenance; prediction serving; real-time personalization; latency-accuracy tradeoffs and edge computing for large-scale models; machine learning lifecycle management.

Data cleaning, data transformation, and crowdsourcing

Human-data interaction including interactive transformation, query authoring, and crowdsourcing; machine learning for data cleaning; statistical properties of data cleaning pipelines; end-to-end systems for crowdsourcing.

Interactive data exploration and visualization

Interactive querying and direct manipulation; scalable spreadsheets and data visualization; languages and interfaces for interactive exploration; progressive query visualization; predictive interaction.

Secure data processing

Data processing under homomorphic encryption; data compression and encryption; differential privacy; oblivious data processing; databases in secure hardware enclaves.

Foundations of data management

Optimal trade-offs between storage, quality, latency, and cost, with applications to crowdsourcing, distributed data management, stream data processing, version management; expressiveness, complexity, and completeness of data representations, query languages, and query processing; query processing with fairness constraints.

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Advances in database systems education: Methods, tools, curricula, and way forward

Muhammad ishaq.

1 Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan

2 Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

3 Department of Computer Science, University of Management and Technology, Lahore, Pakistan

Muhammad Shoaib Farooq

Muhammad faraz manzoor.

4 Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

Uzma Farooq

Kamran abid.

5 Department of Electrical Engineering, University of the Punjab, Lahore, Pakistan

Mamoun Abu Helou

6 Faculty of Information Technology, Al Istiqlal University, Jericho, Palestine

Associated Data

Not Applicable.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students’ interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.

Introduction

Database systems play a pivotal role in the successful implementation of the information systems to ensure the smooth running of many different organizations and companies (Etemad & Küpçü, 2018 ; Morien, 2006 ). Therefore, at least one course about the fundamentals of database systems is taught in every computing and information systems degree (Nagataki et al., 2013 ). Database System Education (DSE) is concerned with different aspects of data management while developing software (Park et al., 2017 ). The IEEE/ACM computing curricula guidelines endorse 30–50 dedicated hours for teaching fundamentals of design and implementation of database systems so as to build a very strong theoretical and practical understanding of the DSE topics (Cvetanovic et al., 2010 ).

Practically, most of the universities offer one user-oriented course at undergraduate level that covers topics related to the data modeling and design, querying, and a limited number of hours on theory (Conklin & Heinrichs, 2005 ; Robbert & Ricardo, 2003 ), where it is often debatable whether to utilize a design-first or query-first approach. Furthermore, in order to update the course contents, some recent trends, including big data and the notion of NoSQL should also be introduced in this basic course (Dietrich et al., 2008 ; Garcia-Molina, 2008 ). Whereas, the graduate course is more theoretical and includes topics related to DB architecture, transactions, concurrency, reliability, distribution, parallelism, replication, query optimization, along with some specialized classes.

Researchers have designed a variety of tools for making different concepts of introductory database course more interesting and easier to teach and learn interactively (Brusilovsky et al., 2010 ) either using visual support (Nagataki et al., 2013 ), or with the help of gamification (Fisher & Khine, 2006 ). Similarly, the instructors have been improvising different methods to teach (Abid et al., 2015 ; Domínguez & Jaime, 2010 ) and evaluate (Kawash et al., 2020 ) this theoretical and practical course. Also, the emerging and hot topics such as cloud computing and big data has also created the need to revise the curriculum and methods to teach DSE (Manzoor et al., 2020 ).

The research in database systems education has evolved over the years with respect to modern contents influenced by technological advancements, supportive tools to engage the learners for better learning, and improvisations in teaching and assessment methods. Particularly, in recent years there is a shift from self-describing data-driven systems to a problem-driven paradigm that is the bottom-up approach where data exists before being designed. This mainly relies on scientific, quantitative, and empirical methods for building models, while pushing the boundaries of typical data management by involving mathematics, statistics, data mining, and machine learning, thus opening a multidisciplinary perspective. Hence, it is important to devote a few lectures to introducing the relevance of such advance topics.

Researchers have provided useful review articles on other areas including Introductory Programming Language (Mehmood et al., 2020 ), use of gamification (Obaid et al., 2020 ), research trends in the use of enterprise service bus (Aziz et al., 2020 ), and the role of IoT in agriculture (Farooq et al., 2019 , 2020 ) However, to the best of our knowledge, no such study was found in the area of database systems education. Therefore, this study discusses research work published in different areas of database systems education involving curricula, tools, and approaches that have been proposed to teach an introductory course on database systems in an effective manner. The rest of the article has been structured in the following manner: Sect.  2 presents related work and provides a comparison of the related surveys with this study. Section  3 presents the research methodology for this study. Section  4 analyses the major findings of the literature reviewed in this research and categorizes it into different important aspects. Section  5 represents advices for the instructors and future directions. Lastly, Sect.  6 concludes the article.

Related work

Systematic Literature Reviews have been found to be a very useful artifact for covering and understanding a domain. A number of interesting review studies have been found in different fields (Farooq et al., 2021 ; Ishaq et al., 2021 ). Review articles are generally categorized into narrative or traditional reviews (Abid et al., 2016 ; Ramzan et al., 2019 ), systematic literature review (Naeem et al., 2020 ) and meta reviews or mapping study (Aria & Cuccurullo, 2017 ; Cobo et al., 2012 ; Tehseen et al., 2020 ). This study presents a systematic literature review on database system education.

The database systems education has been discussed from many different perspectives which include teaching and learning methods, curriculum development, and the facilitation of instructors and students by developing different tools. For instance, a number of research articles have been published focusing on developing tools for teaching database systems course (Abut & Ozturk, 1997 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Furthermore, few authors have evaluated the DSE tools by conducting surveys and performing empirical experiments so as to gauge the effectiveness of these tools and their degree of acceptance among important stakeholders, teachers and students (Brusilovsky et al., 2010 ; Nelson & Fatimazahra, 2010 ). On the other hand, some case studies have also been discussed to evaluate the effectiveness of the improvised approaches and developed tools. For example, Regueras et al. ( 2007 ) presented a case study using the QUEST system, in which e-learning strategies are used to teach the database course at undergraduate level, while, Myers and Skinner ( 1997 ) identified the conflicts that arise when theories in text books regarding the development of databases do not work on specific applications.

Another important facet of DSE research focuses on the curriculum design and evolution for database systems, whereby (Alrumaih, 2016 ; Bhogal et al., 2012 ; Cvetanovic et al., 2010 ; Sahami et al., 2011 ) have proposed solutions for improvements in database curriculum for the better understanding of DSE among the students, while also keeping the evolving technology into the perspective. Similarly, Mingyu et al. ( 2017 ) have shared their experience in reforming the DSE curriculum by adding topics related to Big Data. A few authors have also developed and evaluated different tools to help the instructors teaching DSE.

There are further studies which focus on different aspects including specialized tools for specific topics in DSE (Mcintyre et al, 1995 ; Nelson & Fatimazahra, 2010 ). For instance, Mcintyre et al. ( 1995 ) conducted a survey about using state of the art software tools to teach advanced relational database design courses at Cleveland State University. However, the authors did not discuss the DSE curricula and pedagogy in their study. Similarly, a review has been conducted by Nelson and Fatimazahra ( 2010 ) to highlight the fact that the understanding of basic knowledge of database is important for students of the computer science domain as well as those belonging to other domains. They highlighted the issues encountered while teaching the database course in universities and suggested the instructors investigate these difficulties so as to make this course more effective for the students. Although authors have discussed and analyzed the tools to teach database, the tools are yet to be categorized according to different methods and research types within DSE. There also exists an interesting systematic mapping study by Taipalus and Seppänen ( 2020 ) that focuses on teaching SQL which is a specific topic of DSE. Whereby, they categorized the selected primary studies into six categories based on their research types. They utilized directed content analysis, such as, student errors in query formulation, characteristics and presentation of the exercise database, specific or non-specific teaching approach suggestions, patterns and visualization, and easing teacher workload.

Another relevant study that focuses on collaborative learning techniques to teach the database course has been conducted by Martin et al. ( 2013 ) This research discusses collaborative learning techniques and adapted it for the introductory database course at the Barcelona School of Informatics. The motive of the authors was to introduce active learning methods to improve learning and encourage the acquisition of competence. However, the focus of the study was only on a few methods for teaching the course of database systems, while other important perspectives, including database curricula, and tools for teaching DSE were not discussed in this study.

The above discussion shows that a considerable amount of research work has been conducted in the field of DSE to propose various teaching methods; develop and test different supportive tools, techniques, and strategies; and to improve the curricula for DSE. However, to the best of our knowledge, there is no study that puts all these relevant and pertinent aspects together while also classifying and discussing the supporting methods, and techniques. This review is considerably different from previous studies. Table ​ Table1 1 highlights the differences between this study and other relevant studies in the field of DSE using ✓ and – symbol reflecting "included" and "not included" respectively. Therefore, this study aims to conduct a systematic mapping study on DSE that focuses on compiling, classifying, and discussing the existing work related to pedagogy, supporting tools, and curricula.

Comparison with other related research articles

Research methodology

In order to preserve the principal aim of this study, which is to review the research conducted in the area of database systems education, a piece of advice has been collected from existing methods described in various studies (Elberzhager et al., 2012 ; Keele et al., 2007 ; Mushtaq et al., 2017 ) to search for the relevant papers. Thus, proper research objectives were formulated, and based on them appropriate research questions and search strategy were formulated as shown in Fig.  1 .

Research objectives

The Following are the research objectives of this study:

• i. To find high quality research work in DSE.
• ii. To categorize different aspects of DSE covered by other researchers in the field.
• iii. To provide a thorough discussion of the existing work in this study to provide useful information in the form of evolution, teaching guidelines, and future research directions of the instructors.

Research questions

In order to fulfill the research objectives, some relevant research questions have been formulated. These questions along with their motivations have been presented in Table ​ Table2 2 .

Study selection results

Search strategy

The Following search string used to find relevant articles to conduct this study. “Database” AND (“System” OR “Management”) AND (“Education*” OR “Train*” OR “Tech*” OR “Learn*” OR “Guide*” OR “Curricul*”).

Articles have been taken from different sources i.e. IEEE, Springer, ACM, Science Direct and other well-known journals and conferences such as Wiley Online Library, PLOS and ArXiv. The planning for search to find the primary study in the field of DSE is a vital task.

Study selection

A total of 29,370 initial studies were found. These articles went through a selection process, and two authors were designated to shortlist the articles based on the defined inclusion criteria as shown in Fig.  2 . Their conflicts were resolved by involving a third author; while the inclusion/exclusion criteria were also refined after resolving the conflicts as shown in Table ​ Table3. 3 . Cohen’s Kappa coefficient 0.89 was observed between the two authors who selected the articles, which reflects almost perfect agreement between them (Landis & Koch, 1977 ). While, the number of papers in different stages of the selection process for all involved portals has been presented in Table ​ Table4 4 .

Selection criteria

Title based search: Papers that are irrelevant based on their title are manually excluded in the first stage. At this stage, there was a large portion of irrelevant papers. Only 609 papers remained after this stage.

Abstract based search: At this stage, abstracts of the selected papers in the previous stage are studied and the papers are categorized for the analysis along with research approach. After this stage only 152 papers were left.

Full text based analysis: Empirical quality of the selected articles in the previous stage is evaluated at this stage. The analysis of full text of the article has been conducted. The total of 70 papers were extracted from 152 papers for primary study. Following questions are defined for the conduction of final data extraction.

Quality assessment criteria

Following are the criteria used to assess the quality of the selected primary studies. This quality assessment was conducted by two authors as explained above.

• The study focuses on curricula, tools, approach, or assessments in DSE, the possible answers were Yes (1), No (0)
• The study presents a solution to the problem in DSE, the possible answers to this question were Yes (1), Partially (0.5), No (0)
• The study focuses on empirical results, Yes (1), No (0)

Score pattern of publication channels

Almost 50.00% of papers had scored more than average and 33.33% of papers had scored between the average range i.e., 2.50–3.50. Some articles with the score below 2.50 have also been included in this study as they present some useful information and were published in education-based journals. Also, these studies discuss important demography and technology based aspects that are directly related to DSE.

Threats to validity

The validity of this study could be influenced by the following factors during the literature of this publication.

Construct validity

In this study this validity identifies the primary study for research (Elberzhager et al., 2012 ). To ensure that many primary studies have been included in this literature two authors have proposed possible search keywords in multiple repetitions. Search string is comprised of different terms related to DS and education. Though, list might be incomplete, count of final papers found can be changed by the alternative terms (Ampatzoglou et al., 2013 ). IEEE digital library, Science direct, ACM digital library, Wiley Online Library, PLOS, ArXiv and Google scholar are the main libraries where search is done. We believe according to the statistics of search engines of literature the most research can be found on these digital libraries (Garousi et al., 2013 ). Researchers also searched related papers in main DS research sites (VLDB, ICDM, EDBT) in order to minimize the risk of missing important publication.

Including the papers that does not belong to top journals or conferences may reduce the quality of primary studies in this research but it indicates that the representativeness of the primary studies is improved. However, certain papers which were not from the top publication sources are included because of their relativeness wisth the literature, even though they reduce the average score for primary studies. It also reduces the possibility of alteration of results which might have caused by the improper handling of duplicate papers. Some cases of duplications were found which were inspected later whether they were the same study or not. The two authors who have conducted the search has taken the final decision to the select the papers. If there is no agreement between then there must be discussion until an agreement is reached.

Internal validity

This validity deals with extraction and data analysis (Elberzhager et al., 2012 ). Two authors carried out the data extraction and primary studies classification. While the conflicts between them were resolved by involving a third author. The Kappa coefficient was 0.89, according to Landis and Koch ( 1977 ), this value indicates almost perfect level of agreement between the authors that reduces this threat significantly.

Conclusion validity

This threat deals with the identification of improper results which may cause the improper conclusions. In this case this threat deals with the factors like missing studies and wrong data extraction (Ampatzoglou et al., 2013 ). The objective of this is to limit these factors so that other authors can perform study and produce the proper conclusions (Elberzhager et al., 2012 ).

Interpretation of results might be affected by the selection and classification of primary studies and analyzing the selected study. Previous section has clearly described each step performed in primary study selection and data extraction activity to minimize this threat. The traceability between the result and data extracted was supported through the different charts. In our point of view, slight difference based on the publication selection and misclassification would not alter the main results.

External validity

This threat deals with the simplification of this research (Mateo et al., 2012 ). The results of this study were only considered that related to the DSE filed and validation of the conclusions extracted from this study only concerns the DSE context. The selected study representativeness was not affected because there was no restriction on time to find the published research. Therefore, this external validity threat is not valid in the context of this research. DS researchers can take search string and the paper classification scheme represented in this study as an initial point and more papers can be searched and categorized according to this scheme.

Analysis of compiled research articles

This section presents the analysis of the compiled research articles carefully selected for this study. It presents the findings with respect to the research questions described in Table ​ Table2 2 .

Selection results

A total of 70 papers were identified and analyzed for the answers of RQs described above. Table ​ Table6 6 represents a list of the nominated papers with detail of the classification results and their quality assessment scores.

Classification and quality assessment of selected articles

RQ1.Categorization of research work in DSE field

The analysis in this study reveals that the literature can be categorized as: Tools: any additional application that helps instructors in teaching and students in learning. Methods: any improvisation aimed at improving pedagogy or cognition. Curriculum: refers to the course content domains and their relative importance in a degree program, as shown in Fig.  3 .

Taxonomy of DSE study types

Most of the articles provide a solution by gathering the data and also prove the novelty of their research through results. These papers are categorized as experiments w.r.t. their research types. Whereas, some of them case study papers which are used to generate an in depth, multifaceted understanding of a complex issue in its real-life context, while few others are review studies analyzing the previously used approaches. On the other hand, a majority of included articles have evaluated their results with the help of experiments, while others conducted reviews to establish an opinion as shown in Fig.  4 .

Cross Mapping of DSE study type and research Types

Educational tools, especially those related to technology, are making their place in market faster than ever before (Calderon et al., 2011 ). The transition to active learning approaches, with the learner more engaged in the process rather than passively taking in information, necessitates a variety of tools to help ensure success. As with most educational initiatives, time should be taken to consider the goals of the activity, the type of learners, and the tools needed to meet the goals. Constant reassessment of tools is important to discover innovation and reforms that improve teaching and learning (Irby & Wilkerson, 2003 ). For this purpose, various type of educational tools such as, interactive, web-based and game based have been introduced to aid the instructors in order to explain the topic in more effective way.

The inclusion of technology into the classroom may help learners to compete in the competitive market when approaching the start of their career. It is important for the instructors to acknowledge that the students are more interested in using technology to learn database course instead of merely being taught traditional theory, project, and practice-based methods of teaching (Adams et al., 2004 ). Keeping these aspects in view many authors have done significant research which includes web-based and interactive tools to help the learners gain better understanding of basic database concepts.

Great research has been conducted with the focus of students learning. In this study we have discussed the students learning supportive with two major finding’s objectives i.e., tools which prove to be more helpful than other tools. Whereas, proposed tools with same outcome as traditional classroom environment. Such as, Abut and Ozturk ( 1997 ) proposed an interactive classroom environment to conduct database classes. The online tools such as electronic “Whiteboard”, electronic textbooks, advance telecommunication networks and few other resources such as Matlab and World Wide Web were the main highlights of their proposed smart classroom. Also, Pahl et al. ( 2004 ) presented an interactive multimedia-based system for the knowledge and skill oriented Web-based education of database course students. The authors had differentiated their proposed classroom environment from traditional classroom-based approach by using tool mediated independent learning and training in an authentic setting. On the other hand, some authors have also evaluated the educational tools based on their usage and impact on students’ learning. For example, Brusilovsky et al. ( 2010 )s evaluated the technical and conceptual difficulties of using several interactive educational tools in the context of a single course. A combined Exploratorium has been presented for database courses and an experimental platform, which delivers modified access to numerous types of interactive learning activities.

Also, Taipalus and Perälä ( 2019 ) investigated the types of errors that are persistent in writing SQL by the students. The authors also contemplated the errors while mapping them onto different query concepts. Moreover, Abelló Gamazo et al. ( 2016 ) presented a software tool for the e-assessment of relational database skills named LearnSQL. The proposed software allows the automatic and efficient e-learning and e-assessment of relational database skills. Apart from these, Yue ( 2013 ) proposed the database tool named Sakila as a unified platform to support instructions and multiple assignments of a graduate database course for five semesters. According to this study, students find this tool more useful and interesting than the highly simplified databases developed by the instructor, or obtained from textbook. On the other hand, authors have proposed tools with the main objective to help the student’s grip on the topic by addressing the pedagogical problems in using the educational tools. Connolly et al. ( 2005 ) discussed some of the pedagogical problems sustaining the development of a constructive learning environment using problem-based learning, a simulation game and interactive visualizations to help teach database analysis and design. Also, Yau and Karim ( 2003 ) proposed smart classroom with prevalent computing technology which will facilitate collaborative learning among the learners. The major aim of this smart classroom is to improve the quality of interaction between the instructors and students during lecture.

Student satisfaction is also an important factor for the educational tools to more effective. While it supports in students learning process it should also be flexible to achieve the student’s confidence by making it as per student’s needs (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Also, Cvetanovic et al. ( 2010 ) has proposed a web-based educational system named ADVICE. The proposed solution helps the students to reduce the gap between DBMS, theory and its practice. On the other hand, authors have enhanced the already existing educational tools in the traditional classroom environment to addressed the student’s concerns (Nelson & Fatimazahra, 2010 ; Regueras et al., 2007 ) Table ​ Table7 7 .

Tools: Adopted in DSE and their impacts

Hands on database development is the main concern in most of the institute as well as in industry. However, tools assisting the students in database development and query writing is still major concern especially in SQL (Brusilovsky et al., 2010 ; Nagataki et al., 2013 ).

Student’s grades reflect their conceptual clarity and database development skills. They are also important to secure jobs and scholarships after passing out, which is why it is important to have the educational learning tools to help the students to perform well in the exams (Cvetanovic et al., 2010 ; Taipalus et al., 2018 ). While, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Subsequently, existing educational tools needs to be upgraded or replaced by the more suitable assessment oriented interactive tools to attend challenging students needs (Pahl et al., 2004 ; Yuelan et al., 2011 ).

One other objective of developing the educational tools is to increase the interaction between the students and the instructors. In the modern era, almost every institute follows the student centered learning(SCL). In SCL the interaction between students and instructor increases with most of the interaction involves from the students. In order to support SCL the educational based interactive and web-based tools need to assign more roles to students than the instructors (Abbasi et al., 2016 ; Taipalus & Perälä, 2019 ; Yau & Karim, 2003 ).

Theory versus practice is still one of the main issues in DSE teaching methods. The traditional teaching method supports theory first and then the concepts learned in the theoretical lectures implemented in the lab. Whereas, others think that it is better to start by teaching how to write query, which should be followed by teaching the design principles for database, while a limited amount of credit hours are also allocated for the general database theory topics. This part of the article discusses different trends of teaching and learning style along with curriculum and assessments methods discussed in DSE literature.

A variety of teaching methods have been designed, experimented, and evaluated by different researchers (Yuelan et al., 2011 ; Chen et al., 2012 ; Connolly & Begg, 2006 ). Some authors have reformed teaching methods based on the requirements of modern way of delivering lectures such as Yuelan et al. ( 2011 ) reform teaching method by using various approaches e.g. a) Modern ways of education: includes multimedia sound, animation, and simulating the process and working of database systems to motivate and inspire the students. b) Project driven approach: aims to make the students familiar with system operations by implementing a project. c) Strengthening the experimental aspects: to help the students get a strong grip on the basic knowledge of database and also enable them to adopt a self-learning ability. d) Improving the traditional assessment method: the students should turn in their research and development work as the content of the exam, so that they can solve their problem on their own.

The main aim of any teaching method is to make student learn the subject effectively. Student must show interest in order to gain something from the lectures delivered by the instructors. For this, teaching methods should be interactive and interesting enough to develop the interest of the students in the subject. Students can show interest in the subject by asking more relative questions or completing the home task and assignments on time. Authors have proposed few teaching methods to make topic more interesting such as, Chen et al. ( 2012 ) proposed a scaffold concept mapping strategy, which considers a student’s prior knowledge, and provides flexible learning aids (scaffolding and fading) for reading and drawing concept maps. Also, Connolly & Begg (200s6) examined different problems in database analysis and design teaching, and proposed a teaching approach driven by principles found in the constructivist epistemology to overcome these problems. This constructivist approach is based on the cognitive apprenticeship model and project-based learning. Similarly, Domínguez & Jaime ( 2010 ) proposed an active method for database design through practical tasks development in a face-to-face course. They analyzed results of five academic years using quasi experimental. The first three years a traditional strategy was followed and a course management system was used as material repository. On the other hand, Dietrich and Urban ( 1996 ) have described the use of cooperative group learning concepts in support of an undergraduate database management course. They have designed the project deliverables in such a way that students develop skills for database implementation. Similarly, Zhang et al. ( 2018 ) have discussed several effective classroom teaching measures from the aspects of the innovation of teaching content, teaching methods, teaching evaluation and assessment methods. They have practiced the various teaching measures by implementing the database technologies and applications in Qinghai University. Moreover, Hou and Chen ( 2010 ) proposed a new teaching method based on blending learning theory, which merges traditional and constructivist methods. They adopted the method by applying the blending learning theory on Access Database programming course teaching.

Problem solving skills is a key aspect to any type of learning at any age. Student must possess this skill to tackle the hurdles in institute and also in industry. Create mind and innovative students find various and unique ways to solve the daily task which is why they are more likeable to secure good grades and jobs. Authors have been working to introduce teaching methods to develop problem solving skills in the students(Al-Shuaily, 2012 ; Cai & Gao, 2019 ; Martinez-González & Duffing, 2007 ; Gudivada et al., 2007 ). For instance, Al-Shuaily ( 2012 ) has explored four cognitive factors such as i) Novices’ ability in understanding, ii) Novices’ ability to translate, iii) Novice’s ability to write, iv) Novices’ skills that might influence SQL teaching, and learning methods and approaches. Also, Cai and Gao ( 2019 ) have reformed the teaching method in the database course of two higher education institutes in China. Skills and knowledge, innovation ability, and data abstraction were the main objective of their study. Similarly, Martinez-González and Duffing ( 2007 ) analyzed the impact of convergence of European Union (EU) in different universities across Europe. According to their study, these institutes need to restructure their degree program and teaching methodologies. Moreover, Gudivada et al. ( 2007 ) proposed a student’s learning method to work with the large datasets. they have used the Amazon Web Services API and.NET/C# application to extract a subset of the product database to enhance student learning in a relational database course.

On the other hand, authors have also evaluated the traditional teaching methods to enhance the problem-solving skills among the students(Eaglestone & Nunes, 2004 ; Wang & Chen, 2014 ; Efendiouglu & Yelken, 2010 ) Such as, Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a database design course at Sheffield University and discussed some of the issues they faced, regarding teaching, learning and assessments. Likewise, Wang and Chen ( 2014 ) summarized the problems mainly in teaching of the traditional database theory and application. According to the authors the teaching method is outdated and does not focus on the important combination of theory and practice. Moreover, Efendiouglu and Yelken ( 2010 ) investigated the effects of two different methods Programmed Instruction (PI) and Meaningful Learning (ML) on primary school teacher candidates’ academic achievements and attitudes toward computer-based education, and to define their views on these methods. The results show that PI is not favoured for teaching applications because of its behavioural structure Table ​ Table8 8 .

Methods: Teaching approaches adopted in DSE

Students become creative and innovative when the try to study on their own and also from different resources rather than curriculum books only. In the modern era, there are various resources available on both online and offline platforms. Modern teaching methods must emphasize on making the students independent from the curriculum books and educate them to learn independently(Amadio et al., 2003 ; Cai & Gao, 2019 ; Martin et al., 2013 ). Also, in the work of Kawash et al. ( 2020 ) proposed he group study-based learning approach called Graded Group Activities (GGAs). In this method students team up in order to take the exam as a group. On the other hand, few studies have emphasized on course content to prepare students for the final exams such as, Zheng and Dong ( 2011 ) have discussed the issues of computer science teaching with particular focus on database systems, where different characteristics of the course, teaching content and suggestions to teach this course effectively have been presented.

As technology is evolving at rapid speed, so students need to have practical experience from the start. Basic theoretical concepts of database are important but they are of no use without its implementation in real world projects. Most of the students study in the institutes with the aim of only clearing the exams with the help of theoretical knowledge and very few students want to have practical experience(Wang & Chen, 2014 ; Zheng & Dong, 2011 ). To reduce the gap between the theory and its implementation, authors have proposed teaching methods to develop the student’s interest in the real-world projects (Naik & Gajjar, 2021 ; Svahnberg et al., 2008 ; Taipalus et al., 2018 ). Moreover, Juxiang and Zhihong ( 2012 ) have proposed that the teaching organization starts from application scenarios, and associate database theoretical knowledge with the process from analysis, modeling to establishing database application. Also, Svahnberg et al. ( 2008 ) explained that in particular conditions, there is a possibility to use students as subjects for experimental studies in DSE and influencing them by providing responses that are in line with industrial practice.

On the other hand, Nelson et al. ( 2003 ) evaluated the different teaching methods used to teach different modules of database in the School of Computing and Technology at the University of Sunder- land. They outlined suggestions for changes to the database curriculum to further integrate research and state-of-the-art systems in databases.

• III. Curriculum

Database curriculum has been revisited many times in the form of guidelines that not only present the contents but also suggest approximate time to cover different topics. According to the ACM curriculum guidelines (Lunt et al., 2008 ) for the undergraduate programs in computer science, the overall coverage time for this course is 46.50 h distributed in such a way that 11 h is the total coverage time for the core topics such as, Information Models (4 core hours), Database Systems (3 core hours) and Data Modeling (4 course hours). Whereas, the remaining hours are allocated for elective topics such as Indexing, Relational Databases, Query Languages, Relational Database Design, Transaction Processing, Distributed Databases, Physical Database Design, Data Mining, Information Storage and Retrieval, Hypermedia, Multimedia Systems, and Digital Libraries(Marshall, 2012 ). While, according to the ACM curriculum guidelines ( 2013 ) for undergraduate programs in computer science, this course should be completed in 15 weeks with two and half hour lecture per week and lab session of four hours per week on average (Brady et al., 2004 ). Thus, the revised version emphasizes on the practice based learning with the help of lab component. Numerous organizations have exerted efforts in this field to classify DSE (Dietrich et al., 2008 ). DSE model curricula, bodies of knowledge (BOKs), and some standardization aspects in this field are discussed below:

Model curricula

There are standard bodies who set the curriculum guidelines for teaching undergraduate degree programs in computing disciplines. Curricula which include the guidelines to teach database are: Computer Engineering Curricula (CEC) (Meier et al., 2008 ), Information Technology Curricula (ITC) (Alrumaih, 2016 ), Computing Curriculum Software Engineering (CCSE) (Meyer, 2001 ), Cyber Security Curricula (CSC) (Brady et al., 2004 ; Bishop et al., 2017 ).

Bodies of knowledge (BOK)

A BOK includes the set of thoughts and activities related to the professional area, while in model curriculum set of guidelines are given to address the education issues (Sahami et al., 2011 ). Database body of Knowledge comprises of (a) The Data Management Body of Knowledge (DM- BOK), (b) Software Engineering Education Knowledge (SEEK) (Sobel, 2003 ) (Sobel, 2003 ), and (c) The SE body of knowledge (SWEBOK) (Swebok Evolution: IEEE Computer Society n.d. ).

Apart from the model curricula, and bodies of knowledge, there also exist some standards related to the database and its different modules: ISO/IEC 9075–1:2016 (Computing Curricula, 1991 ), ISO/IEC 10,026–1: 1998 (Suryn, 2003 ).

We also utilize advices from some studies (Elberzhager et al., 2012 ; Keele et al., 2007 ) to search for relevant papers. In order to conduct this systematic study, it is essential to formulate the primary research questions (Mushtaq et al., 2017 ). Since the data management techniques and software are evolving rapidly, the database curriculum should also be updated accordingly to meet these new requirements. Some authors have described ways of updating the content of courses to keep pace with specific developments in the field and others have developed new database curricula to keep up with the new data management techniques.

Furthermore, some authors have suggested updates for the database curriculum based on the continuously evolving technology and introduction of big data. For instance Bhogal et al. ( 2012 ) have shown that database curricula need to be updated and modernized, which can be achieved by extending the current database concepts that cover the strategies to handle the ever changing user requirements and how database technology has evolved to meet the requirements. Likewise, Picciano ( 2012 ) examines the evolving world of big data and analytics in American higher education. According to the author, the “data driven” decision making method should be used to help the institutes evaluate strategies that can improve retention and update the curriculum that has big data basic concepts and applications, since data driven decision making has already entered in the big data and learning analytic era. Furthermore, Marshall ( 2011 ) presented the challenges faced when developing a curriculum for a Computer Science degree program in the South African context that is earmarked for international recognition. According to the author, the Curricula needs to adhere both to the policy and content requirements in order to be rated as being of a particular quality.

Similarly, some studies (Abourezq & Idrissi, 2016 ; Mingyu et al., 2017 ) described big data influence from a social perspective and also proceeded with the gaps in database curriculum of computer science, especially, in the big data era and discovers the teaching improvements in practical and theoretical teaching mode, teaching content and teaching practice platform in database curriculum. Also Silva et al. ( 2016 ) propose teaching SQL as a general language that can be used in a wide range of database systems from traditional relational database management systems to big data systems.

On the other hand, different authors have developed a database curriculum based on the different academic background of students. Such as, Dean and Milani ( 1995 ) have recommended changes in computer science curricula based on the practice in United Stated Military Academy (USMA). They emphasized greatly on the practical demonstration of the topic rather than the theoretical explanation. Especially, for the non-computer science major students. Furthermore, Urban and Dietrich ( 2001 ) described the development of a second course on database systems for undergraduates, preparing students for the advanced database concepts that they will exercise in the industry. They also shared their experience with teaching the course, elaborating on the topics and assignments. Also, Andersson et al. ( 2019 ) proposed variations in core topics of database management course for the students with the engineering background. Moreover, Dietrich et al. ( 2014 ) described two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is that the educators, in diverse academic disciplines, should be able to incorporate these animations in their existing courses to meet their pedagogical needs.

The information systems have evolved into large scale distributed systems that store and process a huge amount of data across different servers, and process them using different distributed data processing frameworks. This evolution has given birth to new paradigms in database systems domain termed as NoSQL and Big Data systems, which significantly deviate from conventional relational and distributed database management systems. It is pertinent to mention that in order to offer a sustainable and practical CS education, these new paradigms and methodologies as shown in Fig.  5 should be included into database education (Kleiner, 2015 ). Tables ​ Tables9 9 and ​ and10 10 shows the summarized findings of the curriculum based reviewed studies. This section also proposed appropriate text book based on the theory, project, and practice-based teaching methodology as shown in Table ​ Table9. 9 . The proposed books are selected purely on the bases of their usage in top universities around the world such as, Massachusetts Institute of Technology, Stanford University, Harvard University, University of Oxford, University of Cambridge and, University of Singapore and the coverage of core topics mentioned in the database curriculum.

Concepts in Database Systems Education (Kleiner, 2015 )

Recommended text books for DSE

Curriculum: Findings of Reviewed Literature

RQ.2 Evolution of DSE research

This section discusses the evolution of database while focusing the DSE over the past 25 years as shown in Fig.  6 .

Evolution of DSE studies

This study shows that there is significant increase in research in DSE after 2004 with 78% of the selected papers are published after 2004. The main reason of this outcome is that some of the papers are published in well-recognized channels like IEEE Transactions on Education, ACM Transactions on Computing Education, International Conference on Computer Science and Education (ICCSE), and Teaching, Learning and Assessment of Database (TLAD) workshop. It is also evident that several of these papers were published before 2004 and only a few articles were published during late 1990s. This is because of the fact that DSE started to gain interest after the introduction of Body of Knowledge and DSE standards. The data intensive scientific discovery has been discussed as the fourth paradigm (Hey et al., 2009 ): where the first involves empirical science and observations; second contains theoretical science and mathematically driven insights; third considers computational science and simulation driven insights; while the fourth involves data driven insights of modern scientific research.

Over the past few decades, students have gone from attending one-room class to having the world at their fingertips, and it is a great challenge for the instructors to develop the interest of students in learning database. This challenge has led to the development of the different types of interactive tools to help the instructors teach DSE in this technology oriented era. Keeping the importance of interactive tools in DSE in perspective, various authors have proposed different interactive tools over the years, such as during 1995–2003, when different authors proposed various interactive tools. Some studies (Abut & Ozturk, 1997 ; Mcintyre et al., 1995 ) introduced state of the art interactive tools to teach and enhance the collaborative learning among the students. Similarly, during 2004–2005 more interactive tools in the field of DSE were proposed such as Pahl et al. ( 2004 ), Connolly et al. ( 2005 ) introduced multimedia system based interactive model and game based collaborative learning environment.

The Internet has started to become more common in the first decade of the twenty-first century and its positive impact on the education sector was undeniable. Cost effective, student teacher peer interaction, keeping in touch with the latest information were the main reasons which made the instructors employ web-based tools to teach database in the education sector. Due to this spike in the demand of web-based tools, authors also started to introduce new instruments to assist with teaching database. In 2007 Regueras et al. ( 2007 ) proposed an e-learning tool named QUEST with a feedback module to help the students to learn from their mistakes. Similarly, in 2010, multiple authors have proposed and evaluated various web-based tools. Cvetanovic et al. ( 2010 ) proposed ADVICE with the functionality to monitor student’s progress, while, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Furthermore, Nelson and Fatimazahra ( 2010 ) evaluated different web-based tools to highlight the complexities of using these web-based instruments.

Technology has changed the teaching methods in the education sector but technology cannot replace teachers, and despite the amount of time most students spend online, virtual learning will never recreate the teacher-student bond. In the modern era, innovation in technology used in educational sectors is not meant to replace the instructors or teaching methods.

During the 1990s some studies (Dietrich & Urban, 1996 ; Urban & Dietrich, 1997 ) proposed learning and teaching methods respectively keeping the evolving technology in view. The highlight of their work was project deliverables and assignments where students progressively advanced to a step-by-step extension, from a tutorial exercise and then attempting more difficult extension of assignment.

During 2002–2007 various authors have discussed a number of teaching and learning methods to keep up the pace with the ever changing database technology, such as Connolly and Begg ( 2006 ) proposing a constructive approach to teach database analysis and design. Similarly, Prince and Felder ( 2006 ) reviewed the effectiveness of inquiry learning, problem based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. Also, McIntyre et al. (Mcintyre et al., 1995 ) brought to light the impact of convergence of European Union (EU) in different universities across Europe. They suggested a reconstruction of teaching and learning methodologies in order to effectively teach database.

During 2008–2013 more work had been done to address the different methods of teaching and learning in the field of DSE, like the work of Dominguez and Jaime ( 2010 ) who proposed an active learning approach. The focus of their study was to develop the interest of students in designing and developing databases. Also, Zheng and Dong ( 2011 ) have highlighted various characteristics of the database course and its teaching content. Similarly, Yuelan et al. ( 2011 ) have reformed database teaching methods. The main focus of their study were the Modern ways of education, project driven approach, strengthening the experimental aspects, and improving the traditional assessment method. Likewise, Al-Shuaily ( 2012 ) has explored 4 cognitive factors that can affect the learning process of database. The main focus of their study was to facilitate the students in learning SQL. Subsequently, Chen et al. ( 2012 ) also proposed scaffolding-based concept mapping strategy. This strategy helps the students to better understand database management courses. Correspondingly, Martin et al. ( 2013 ) discussed various collaborative learning techniques in the field of DSE while keeping database as an introductory course.

In the years between 2014 and 2021, research in the field of DSE increased, which was the main reason that the most of teaching, learning and assessment methods were proposed and discussed during this period. Rashid and Al-Radhy ( 2014 ) discussed the issues of traditional teaching, learning, assessing methods of database courses at different universities in Kurdistan and the main focus of their study being reformation issues, such as absence of teaching determination and contradiction between content and theory. Similarly, Wang and Chen ( 2014 ) summarized the main problems in teaching the traditional database theory and its application. Curriculum assessment mode was the main focus of their study. Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a databases design course at Sheffield University. Their focus of study included was to teach the database design module to a diverse group of students from different backgrounds. Rashid ( 2015 ) discussed some important features of database courses, whereby reforming the conventional teaching, learning, and assessing strategies of database courses at universities were the main focus of this study. Kui et al. ( 2018 ) reformed the teaching mode of database courses based on flipped classroom. Initiative learning of database courses was their main focus in this study. Similarly, Zhang et al. ( 2018 ) discussed several effective classroom teaching measures. The main focus of their study was teaching content, teaching methods, teaching evaluation and assessment methods. Cai and Gao ( 2019 ) also carried out the teaching reforms in the database course of liberal arts. Diversified teaching modes, such as flipping classroom, case oriented teaching and task oriented were the focus of their study. Teaching Kawash et al. ( 2020 ) proposed a learning approach called Graded Group Activities (GGAs). Their main focus of the study was reforming learning and assessment method.

Database course covers several topics that range from data modeling to data implementation and examination. Over the years, various authors have given their suggestions to update these topics in database curriculum to meet the requirements of modern technologies. On the other hand, authors have also proposed a new curriculum for the students of different academic backgrounds and different areas. These reformations in curriculum helped the students in their preparation, practically and theoretically, and enabled them to compete in the competitive market after graduation.

During 2003 and 2006 authors have proposed various suggestions to update and develop computer science curriculum across different universities. Robbert and Ricardo ( 2003 ) evaluated three reviews from 1999 to 2002 that were given to the groups of educators. The focus of their study was to highlight the trends that occurred in database curriculum. Also, Calero et al. ( 2003 ) proposed a first draft for this Database Body of Knowledge (DBBOK). Database (DB), Database Design (DBD), Database Administration (DBAd), Database Application (DBAp) and Advance Databases (ADVDB) were the main focus of their study. Furthermore, Conklin and Heinrichs (Conklin & Heinrichs, 2005 ) compared the content included in 13 database textbooks and the main focus of their study was IS 2002, CC2001, and CC2004 model curricula.

The years from 2007 and 2011, authors managed to developed various database curricula, like Luo et al. ( 2008 ) developed curricula in Zhejiang University City College. The aim of their study to nurture students to be qualified computer scientists. Likewise, Dietrich et al. ( 2008 ) proposed the techniques to assess the development of an advanced database course. The purpose behind the addition of an advanced database course at undergraduate level was to prepare the students to respond to industrial requirements. Also, Marshall ( 2011 ) developed a new database curriculum for Computer Science degree program in the South African context.

During 2012 and 2021 various authors suggested updates for the database curriculum such as Bhogal et al. ( 2012 ) who suggested updating and modernizing the database curriculum. Data management and data analytics were the focus of their study. Similarly, Picciano ( 2012 ) examined the curriculum in the higher level of American education. The focus of their study was big data and analytics. Also, Zhanquan et al. ( 2016 ) proposed the design for the course content and teaching methods in the classroom. Massive Open Online Courses (MOOCs) were the focus of their study. Likewise, Mingyu et al. ( 2017 ) suggested updating the database curriculum while keeping new technology concerning the database in perspective. The focus of their study was big data.

The above discussion clearly shows that the SQL is most discussed topic in the literature where more than 25% of the studies have discussed it in the previous decade as shown in Fig.  7 . It is pertinent to mention that other SQL databases such as Oracle, MS access are discussed under the SQL banner (Chen et al., 2012 ; Hou & Chen, 2010 ; Wang & Chen, 2014 ). It is mainly because of its ability to handle data in a relational database management system and direct implementation of database theoretical concepts. Also, other database topics such as transaction management, application programming etc. are also the main highlights of the topics discussed in the literature.

Evolution of Database topics discussed in literature

Research synthesis, advice for instructors, and way forward

This section presents the synthesized information extracted after reading and analyzing the research articles considered in this study. To this end, it firstly contextualizes the tools and methods to help the instructors find suitable tools and methods for their settings. Similarly, developments in curriculum design have also been discussed. Subsequently, general advice for instructors have been discussed. Lastly, promising future research directions for developing new tools, methods, and for revising the curriculum have also been discussed in this section.

Methods, tools, and curriculum

Methods and tools.

Web-based tools proposed by Cvetanovic et al. ( 2010 ) and Wang et al. ( 2010 ) have been quite useful, as they are growing increasingly pertinent as online mode of education is prevalent all around the globe during COVID-19. On the other hand, interactive tools and smart class room methodology has also been used successfully to develop the interest of students in database class. (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ; Canedo et al., 2021 ; Ko et al., 2021 ).

One of the most promising combination of methodology and tool has been proposed by Cvetanovic et al. ( 2010 ), whereby they developed a tool named ADVICE that helps students learn and implement database concepts while using project centric methodology, while a game based collaborative learning environment was proposed by Connolly et al. ( 2005 ) that involves a methodology comprising of modeling, articulation, feedback, and exploration. As a whole, project centric teaching (Connolly & Begg, 2006 ; Domínguez & Jaime, 2010 ) and teaching database design and problem solving skills Wang and Chen ( 2014 ), are two successful approaches for DSE. Whereas, other studies (Urban & Dietrich, 1997 ) proposed teaching methods that are more inclined towards practicing database concepts. While a topic specific approach has been proposed by Abbasi et al. ( 2016 ), Taipalus et al. ( 2018 ) and Silva et al. ( 2016 ) to teach and learn SQL. On the other hand, Cai and Gao ( 2019 ) developed a teaching method for students who do not have a computer science background. Lastly, some useful ways for defining assessments for DSE have been proposed by Kawash et al. ( 2020 ) and Zhang et al. ( 2018 ).

Curriculum of database adopted by various institutes around the world does not address how to teach the database course to the students who do not have a strong computer science background. Such as Marshall ( 2012 ), Luo et al. ( 2008 ) and Zhanquan et al. ( 2016 ) have proposed the updates in current database curriculum for the students who are not from computer science background. While Abid et al. ( 2015 ) proposed a combined course content and various methodologies that can be used for teaching database systems course. On the other hand, current database curriculum does not include the topics related to latest technologies in database domain. This factor was discussed by many other studies as well (Bhogal et al., 2012 ; Mehmood et al., 2020 ; Picciano, 2012 ).

Guidelines for instructors

The major conclusion of this study are the suggestions based on the impact and importance for instructors who are teaching DSE. Furthermore, an overview of productivity of every method can be provided by the empirical studies. These instructions are for instructors which are the focal audience of this study. These suggestions are subjective opinions after literature analysis in form of guidelines according to the authors and their meaning and purpose were maintained. According to the literature reviewed, various issues have been found in this section. Some other issues were also found, but those were not relevant to DSE. Following are some suggestions that provide interesting information:

Project centric and applied approach

• To inculcate database development skills for the students, basic elements of database development need to be incorporated into teaching and learning at all levels including undergraduate studies (Bakar et al., 2011 ). To fulfill this objective, instructors should also improve the data quality in DSE by assigning the projects and assignments to the students where they can assess, measure and improve the data quality using already deployed databases. They should demonstrate that the quality of data is determined not only by the effective design of a database, but also through the perception of the end user (Mathieu & Khalil, 1997 )
• The gap between the database course theory and industrial practice is big. Fresh graduate students find it difficult to cope up with the industrial pressure because of the contrast between what they have been taught in institutes and its application in industry (Allsopp et al., 2006 ). Involve top performers from classes in industrial projects so that they are able to acquiring sufficient knowledge and practice, especially for post graduate courses. There must be some other activities in which industry practitioners come and present the real projects and also share their industrial experiences with the students. The gap between theoretical and the practical sides of database has been identified by Myers and Skinner ( 1997 ). In order to build practical DS concepts, instructors should provide the students an accurate view of reality and proper tools.

Importance of software development standards and impact of DB in software success

• They should have the strategies, ability and skills that can align the DSE course with the contemporary Global Software Development (GSD) (Akbar & Safdar, 2015 ; Damian et al., 2006 ).
• Enable the students to explain the approaches to problem solving, development tools and methodologies. Also, the DS courses are usually taught in normal lecture format. The result of this method is that students cannot see the influence on the success or failure of projects because they do not realize the importance of DS activities.

Pedagogy and the use of education technology

• Some studies have shown that teaching through play and practical activities helps to improve the knowledge and learning outcome of students (Dicheva et al., 2015 ).
• Interactive classrooms can help the instructors to deliver their lecture in a more effective way by using virtual white board, digital textbooks, and data over network(Abut & Ozturk, 1997 ). We suggest that in order to follow the new concept of smart classroom, instructors should use the experience of Yau and Karim ( 2003 ) which benefits in cooperative learning among students and can also be adopted in DSE.
• The instructors also need to update themselves with full spectrum of technology in education, in general, and for DSE, in particular. This is becoming more imperative as during COVID the world is relying strongly on the use of technology, particularly in education sector.

Periodic Curriculum Revision

• There is also a need to revisit the existing series of courses periodically, so that they are able to offer the following benefits: (a) include the modern day database system concepts; (b) can be offered as a specialization track; (c) a specialized undergraduate degree program may also be designed.

DSE: Way forward

This research combines a significant work done on DSE at one place, thus providing a point to find better ways forward in order to improvise different possible dimensions for improving the teaching process of a database system course in future. This section discusses technology, methods, and modifications in curriculum would most impact the delivery of lectures in coming years.

Several tools have already been developed for effective teaching and learning in database systems. However, there is a great room for developing new tools. Recent rise of the notion of “serious games” is marking its success in several domains. Majority of the research work discussed in this review revolves around web-based tools. The success of serious games invites researchers to explore this new paradigm of developing useful tools for learning and practice database systems concepts.

Likewise, due to COVID-19 the world is setting up new norms, which are expected to affect the methods of teaching as well. This invites the researchers to design, develop, and test flexible tools for online teaching in a more interactive manner. At the same time, it is also imperative to devise new techniques for assessments, especially conducting online exams at massive scale. Moreover, the researchers can implement the idea of instructional design in web-based teaching in which an online classroom can be designed around the learners’ unique backgrounds and effectively delivering the concepts that are considered to be highly important by the instructors.

The teaching, learning and assessment methods discussed in this study can help the instructors to improve their methods in order to teach the database system course in a better way. It is noticed that only 16% of authors have the assessment methods as their focus of study, which clearly highlights that there is still plenty of work needed to be done in this particular domain. Assessment techniques in the database course will help the learners to learn from their mistakes. Also, instructors must realize that there is a massive gap between database theory and practice which can only be reduced with maximum practice and real world database projects.

Similarly, the technology is continuously influencing the development and expansion of modern education, whereas the instructors’ abilities to teach using online platforms are critical to the quality of online education.

In the same way, the ideas like flipped classroom in which students have to prepare the lesson prior to the class can be implemented on web-based teaching. This ensures that the class time can be used for further discussion of the lesson, share ideas and allow students to interact in a dynamic learning environment.

The increasing impact of big data systems, and data science and its anticipated impact on the job market invites the researchers to revisit the fundamental course of database systems as well. There is a need to extend the boundaries of existing contents by including the concepts related to distributed big data systems data storage, processing, and transaction management, with possible glimpse of modern tools and technologies.

As a whole, an interesting and long term extension is to establish a generic and comprehensive framework that engages all the stakeholders with the support of technology to make the teaching, learning, practicing, and assessing easier and more effective.

This SLR presents review on the research work published in the area of database system education, with particular focus on teaching the first course in database systems. The study was carried out by systematically selecting research papers published between 1995 and 2021. Based on the study, a high level categorization presents a taxonomy of the published under the heads of Tools, Methods, and Curriculum. All the selected articles were evaluated on the basis of a quality criteria. Several methods have been developed to effectively teach the database course. These methods focus on improving learning experience, improve student satisfaction, improve students’ course performance, or support the instructors. Similarly, many tools have been developed, whereby some tools are topic based, while others are general purpose tools that apply for whole course. Similarly, the curriculum development activities have also been discussed, where some guidelines provided by ACM/IEEE along with certain standards have been discussed. Apart from this, the evolution in these three areas has also been presented which shows that the researchers have been presenting many different teaching methods throughout the selected period; however, there is a decrease in research articles that address the curriculum and tools in the past five years. Besides, some guidelines for the instructors have also been shared. Also, this SLR proposes a way forward in DSE by emphasizing on the tools: that need to be developed to facilitate instructors and students especially post Covid-19 era, methods: to be adopted by the instructors to close the gap between the theory and practical, Database curricula update after the introduction of emerging technologies such as big data and data science. We also urge that the recognized publication venues for database research including VLDB, ICDM, EDBT should also consider publishing articles related to DSE. The study also highlights the importance of reviving the curricula, tools, and methodologies to cater for recent advancements in the field of database systems.

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Database Design - 2nd Edition

(11 reviews)

Adrienne Watt, City University

Copyright Year: 2014

Publisher: BCcampus

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Simon Jin, Associate Professor, Metropolitan State University on 3/6/24

Considering this is a textbook for introductory class for Database Management/Design, this book well covered most of the necessary basics required for new learners who may not have any prior experience / knowledge. In most cases, however, little... read more

Comprehensiveness rating: 4 see less

Considering this is a textbook for introductory class for Database Management/Design, this book well covered most of the necessary basics required for new learners who may not have any prior experience / knowledge. In most cases, however, little bit more information would be appreciated by the beginner readers: the current length seems to be not sufficient to cover in-depth insight for the concepts.

Content Accuracy rating: 5

In general, the textbook is accurate and unbiased.

Relevance/Longevity rating: 4

This textbook is certainly relevant. Due to the fast evolving nature of the content (i.e., fast evolving nature of IS world), updates will be required. For example, introduction to NoSQL along with comparison between traditional relational database and NoSQL database.

Clarity rating: 3

Text is clear and easy to follow. However, figures may need to be revised. The resolution of included figures is not high enough to be clearly displayed. Furthermore, when clicked, image with same resolution or even the wrong image was displayed which couldn't address the issue of pdf version.

Consistency rating: 5

No inconsistency issues found in the textbook. The terminology used were consistent and relevant to the subject matter.

Modularity rating: 5

Due to the comprehensiveness & modularity of each chapter, each chapter can be used as a single source of class material independently or minimum reference to other chapters.

Organization/Structure/Flow rating: 4

Organization/structure/flow of this textbook is good. With consistent structure of chapter (i.e., Content, Key Terms, and Exercise), it is easy to follow. It would be better to have learning objectives for each chapter, though.

Interface rating: 5

No navigation issues found. Adding glossary and index, however, would help readers locate important concepts more easily.

Grammatical Errors rating: 5

No grammatical issues found.

Cultural Relevance rating: 5

No culturally offensive issues found.

In sum, this textbook is a good resource for new learners in the topic of Database design/management. 3rd edition with more contents and updates would be appreciated.

Reviewed by David Barbella, Assistant Professor, Earlham College on 3/29/21

This is very much a Database Design text, not a Database Implementation text, and in those grounds is reasonably comprehensive. The text covers much of the terminology I would want students to become familiar with, as well as the major concepts... read more

This is very much a Database Design text, not a Database Implementation text, and in those grounds is reasonably comprehensive. The text covers much of the terminology I would want students to become familiar with, as well as the major concepts required for understanding database systems in the abstract. The text is on the shorter side, so some concepts are covered only briefly, or not at all. (For example, transactions and isolation levels are not covered.) There is also relatively little coverage of subqueries or more complex queries in general. The text is not really a guide to implementation or use, and is focused mostly on design. SQL is covered only briefly, and alternatives to SQL not at all. A course covering that material in depth would want to supplement this text. The text is very short - just 126 pages, plus the Appendices. (For comparison, several of the most widely-used traditional textbooks are 500 or more pages.) This brevity has some advantages, but it necessarily means that the coverage of many topics is brief, high-level, and sometimes includes only minimal context.

I did not notice any real errors in the text, although in some places the text gives definitions or descriptions that, while technically correct, may not be particularly useful to an introductory student due to lack of context. The text also has an issue that almost any general explanation of SQL has, which is that SQL implementations vary so much that it's hard to make general statements about the language. The text handles this issue as well as any, however, and doesn't delve into most of the areas where implementations differ the most.

Relevance/Longevity rating: 5

The text generally sticks to timeless design principles, so little about the text comes off as dated. There's a few minor things - for example, the Waterfall method is of important historical interest as one of the earliest attempts to formalize the process of software design, but is no longer considered to be best practice by most people. (Although some methods that are still in use use the method as a foundation.) However, on the whole, I would expect the text to remain largely evergreen.

The text is generally written in a very accessible way - it feels like it was written for humans. One of the text's weaknesses, however, is that it in many places reads like an extended glossary, providing definitions of relevant terms but often very little context for why a beginner to databases should care about those things. Examples are often a bit sparse or under-explained. The middle section of the text is strongest, includes the best and clearest examples, and does the most to connect those examples to the text. The focus on definitions extends to the end-of-chapter exercises, which for much of the text consist heavily of definitions problems, although some later chapters include more application. The approach to exercises varies from chapter to chapter; many chapters include only a single, straightforward application problem, while a few others dig a little deeper.

Consistency rating: 4

While not inconsistent per se, the text introduces a fair amount of terminology and notation that it then does not continue to make use of.

Database Design isn’t necessarily a topic that lends itself especially well to modularity, especially when it comes to the basics, as many concepts build directly on top of each other in ways that make following a certain basic structure sort of unavoidable. That said, to whatever degree modularity is possible within that structure, the text does a fine job achieving it. The decision to not use just one or just a few running examples throughout the text is both a strength and a weakness. It means that individual chapters can more easily be skipped or rearranged, but also means that readers need to internalize new examples as they’re used.

There’s a little bit of redundancy; a few concepts are introduced more than once. On the whole, though, the text generally presents things in as sensible an order as is possible. SQL is introduced fairly late in the text - in chapter 15 of 16. Most of the basic functionality is covered, but in a very cursory way, and with examples that aren’t contextualized or explained. I’m not sure if a student who is not already comfortable with joins, for example, will get anything out of the section describing them. Even in a course that was focused only minimally on implementation, I would move that material much earlier.

Interface rating: 4

At least in the .pdf version, some of the images are a bit difficult to read, although almost all can be read if you zoom in. For the most part, the visuals are clear and clean, and the book's formatting is consistent.

I noticed no serious grammatical issues.

Cultural Relevance rating: 3

The examples used the text are mostly fairly typical, in the business-world and course-management domains. One list of books used in an example does include a novel called “Incubus Dreams,” which is not something I would have included. Name lists don’t suggest a ton of ethnic diversity. Text avoids using gender as an example of a binary field, which is nice. There's no section of the text that specifically touches on ethics or that connects database design to a larger context. That isn't something I'd necessary expect that such a text would definitely have, however.

Reviewed by Amit Deokar, Associate Professor, University of Massachusetts Lowell on 6/29/20

This book is quite comprehensive in its coverage of key topic areas expected to be covered in an introductory database course at the undergraduate (bachelor's degree) level. The authors should also consider including an introduction to star schema... read more

This book is quite comprehensive in its coverage of key topic areas expected to be covered in an introductory database course at the undergraduate (bachelor's degree) level. The authors should also consider including an introduction to star schema and snowflake schema topics in order to introduce datamarts and data warehouses in a separate chapter. Also, another chapter devoted to recent kinds of databases such as various types of NoSQL databases and similarities and differences of these databases (particularly from design and implementation viewpoints) as compared to relational databases would be useful to cover in an introductory database management course. Last, but not least, the authors should consider using an open-source database such as MySQL for demonstration of SQL concepts rather than MS Access (which is a proprietary and a desktop database software).

The technical content in the book is quite accurate. Given that the book is meant for an introductory audience, some of the nuances of database design are not covered (which was expected).

The core concepts in the book will be very relevant for the next several years. However, as pointed out in the comprehensiveness section, the addition of newer topics and the use of open-source software such as MySQL will make the book more relevant for students looking to immediately apply this knowledge in practice in their jobs/internships and so forth.

Clarity rating: 5

Clarity and simplicity are perhaps the key strengths of this book. I like the simple explanations and examples provided by the authors that can help students quickly grasp the gist of the concepts in an intuitive manner.

The overall flow of the book and the writing style is fairly consistent. Students who understand the earlier chapters should be able to continue with the later chapters without much difficulty.

The book itself is quite modular, which is great. However, the nature of the topic area is such that understanding fundamental concepts discussed in earlier chapters is necessary for grasping the concepts discussed in later chapters. This dependency is unavoidable and authors have done a good job in making the book chapters as modular as possible.

This 2nd edition of the book contains 16 chapters. While the book flows very well overall, I believe it would help the adoption of this book for a course, if the book were structured into 12 or 13 chapters (including a couple of new chapters suggested on data warehousing and NoSQL). This will help course instructors to easily use specific chapters of the book for each week. Alternatively, the authors can suggest different pathways for adoption for instructors teaching different lengths of courses (e.g., 6 weeks, 10 weeks, 14 weeks).

There were no major interface issues encountered while using this book. Figures and charts were blurry in some places and the distortion in size made it somewhat difficult to read those portions without interruption.

The book is well-written with no obvious grammatical errors.

The examples in the book are general and diverse and to my knowledge should not be offensive to any community.

To assist instructors in teaching a course who want to adopt this book, I would request adding extensive exercises (along with an instructor's solution manual) and test bank to accompany the book. Ideally, if these materials can be easily integrated into standard course management systems, it would ease the book adoption process.

Reviewed by Thyago Mota, Assistant Professor, Metropolitan State University of Denver on 10/25/19

The book does not cover relational algebra, which provides an important foundation for relational model mechanisms. From my personal experience teaching databases, discussing relational algebra makes it easier for students to later grasp SQL... read more

The book does not cover relational algebra, which provides an important foundation for relational model mechanisms. From my personal experience teaching databases, discussing relational algebra makes it easier for students to later grasp SQL joins. Other topics that I would have liked to have seen covered by this book are triggers, stored procedures, indexes, SQL DCL, security, database programming, and NoSQL.

Content Accuracy rating: 4

The book is error-free but some exercises are Microsoft biased (e.g., the first exercise at the end of Chapter 16 requires students to download an MSI file). This problem can be easily modified by providing an .sql source file with the database schema for the exercise.

The book should have a chapter on NoSQL to make it more up-to-date with current trends in databases.

Clarity rating: 4

Sometimes the book feels a bit "too dry" and this might negatively impact a student's motivation when reading it.

The book's terminology is consistent with the one used in database systems.

I felt that the text was written in such a way that could be easily broken out and adapted to the course that I teach.

Chapter 14 (Database Users) does not have enough content to justify having it as a stand alone chapter. I suggest merging it with another chapter, perhaps with Chapter 13 that talks about software engineering in the context of database systems.

Interface rating: 3

Most images that I clicked (to make them bigger) redirected me to an entirely different picture.

While English is not my first language, I felt the text was grammatically correct.

I didn't find the text to be culturally insensitive or non-inclusive.

I think the authors did a decent job. The text is clear and covers a fair amount of most of the topics commonly listed in introductory database systems courses in CS. I hope the authors continue to add more content, improvements and updates. I will recommend this book to both my colleagues and students and can't wait for the next edition.

Reviewed by Carolyn LaMacchia, Associate Professor, Bloomsburg University of Pennsylvania on 3/9/19

Content walks through the various pieces to build understanding. All components are there for relational database design. read more

Comprehensiveness rating: 5 see less

Content walks through the various pieces to build understanding. All components are there for relational database design.

Contents are accurate and presented without bias.

The content is up to date. Suggestion - the text focus is on designing for operational data. Add a chapter to describe data warehousing and data storage with large volume of data. I am very impressed with the presentation of the concepts. I like that all of the examples of the concepts. I like the assignments and keywords too.

Each concept includes an illustration. I really like this.

Yes, consistent

Yes - For example some chapters can be eliminate - like describing normal form

Organization/Structure/Flow rating: 5

Data modeling is presented in the appropriate sequence. Each section is either independent on includes information presented in an earlier section

Its' a typical pdf. The copy I have does not include and internal navigation.

Grammatical Errors rating: 1

Did not detect errors.

The text is NOT culturally insensitive. I believe I picked the correct response of 5.

I really liked this text. I plan on incorporating it into one of my classes. I will have to supplement a bit to discuss data design for analysis that is fed from a operational database. But, that just is the nature of the course that I teach. I appreciate the effort that went into this book. I sincerely thank the authors for sharing.

Reviewed by Morgan Benton, Associate Professor, JMU on 11/26/18

While the book at least mentions all of the key terms, it is not clear that these concepts are covered in sufficient depth to really serve as a practical guide for new practitioners. More explanation follows. read more

Comprehensiveness rating: 3 see less

While the book at least mentions all of the key terms, it is not clear that these concepts are covered in sufficient depth to really serve as a practical guide for new practitioners. More explanation follows.

I didn't spot any glaring inaccuracies in the book. However, because it was so short, I worry that there was not enough context provided or depth of explanation so that beginners in this field would be able to follow it with any degree of confidence. I think this book would require HEAVY additional guidance from an instructor. The pace of development is so fast these days, students need to be as self-sufficient in their learning as possible, and I'm not sure that is practical with this text.

Relevance/Longevity rating: 3

It is not clear that the authors have spent much time doing database development in the last ten years. Their suggested development methodology based on the waterfall model is all but obsolete. Over time, it has proven to be both inflexible and a bottleneck that delays the efforts of other developers working on a project. Their coverage in some chapters is oddly platform-specific. For example, the data types they introduce in detail in chapter 15 (SQL Structured Query Language) do not apply to all (or perhaps even most) DBMS, and the differences between DBMS implementations are likely to cause major difficulties if students were to try to apply these concepts in a context where they don't apply. SQLite, for example, only has about 3-4 native data types and it is one of the most commonly used environments these days due to its small size, portability, and the fact that it is built-in to browsers and mobile devices.

As explained above in the "Accuracy" section, I worry that the explanations of key concepts were too short, not well organized, and therefore are likely to be unclear to beginners in this field.

Consistency rating: 3

Most of the time the book stays at a very high level, but on occasion, and without warning it jumps into great depth. For example, chapter 11 (functional dependencies) takes a sudden and deep dive into the subject of set theory and related axioms. This is not consistent in tone or apparent level of understanding of the reader. This would be quite jarring from the perspective of a student.

Modularity rating: 3

Although the chapter titles suggest modularity, I didn't feel there was a great deal of discipline in terms of where content was placed. For example, chapter 8 (The Entity Relationship Data Model) appears to be essentially the same as chapter 10 (ER Modeling). I think students being introduced to the concepts here would be very confused by this.

Organization/Structure/Flow rating: 3

I found the organization somewhat confusing. For example, both chapters 10 and 11 begin by introducing the concept of functional dependency. I would have expected the concept to be fully defined and explained in one chapter or the other, not both, or at the very least make it clear that the concept is broken up into multiple chapters. Some topics seemed to appear out of the blue in the middle of some chapters without warning. Chapters were inconsistent in terms of their length and the depth and care with which they treated a subject. For example, chapter 14 (Database Users) was extremely short, and could have been covered (perhaps was?) in a much earlier chapter like chapter 2 (Fundamental Concepts).

In general, navigating through the book was straightforward. However, many of the images were very small and of poor resolution. Furthermore, if you click on many of the images they are linked to different, unrelated images rather than larger, clearer versions of the same image.

I found only minor problems with grammar or diction.

Cultural Relevance rating: 2

This book is not offensive, at all. However, it utterly fails to address the cultural contexts of data within organizations and society. Beginners to database design frequently fail to understand the impact that database structure can have on the structure and function of an organization. Sometimes organizations find themselves having to adapt to their data structures rather than the other way around. A good modern example is the concept of gender. It is now generally recognized that gender is a non-binary facet of identity. An otherwise well-meaning developer who reduced gender to "male/female" may cause unintended harm to the people whose data is being stored in a database. The traditional failure of the software community to consider, let alone address, issues like this is replete through the industry. Discussions of the ethical and sociocultural ramifications of data are completely absent from this text.

While technically fairly accurate, this book falls short in some important dimensions of relevance and cultural sensitivity. I think the treatment of the subjects is uneven, redundant in some places, very high level in others, and of more depth than necessary in yet others. I would not feel comfortable using this book to teach introductory students.

Reviewed by Sally Hamouda, Assistant Professor, Rhode Island College on 2/1/18

The texts covers all the topics required for an introduction to data base management course. read more

The texts covers all the topics required for an introduction to data base management course.

The book is accurate and follows the conventions used in other popular references in the data base management system field.

The book is very relevant to the content covered in an introductory database management system courses.

The book text clear. The figures resolution is not excellent but readable. The naming of the attributes for some relations in the relational model chapter is not very descriptive in some cases. The exercises are limited in some chapters.

The naming of the attributes for some relations in the relational model chapter is not very descriptive in some cases. The exercises are limited in some chapters.

The text is easy to read and easy to divide in smaller sections that can be assigned within the course.

The flow of the book is very good and follows the state of the art for other very well know references in the same field.

The only thing to mention is the resolution of the figures that need to be enhanced.

I didn't see any grammatical mistakes.

The text is not culturally insensitive or offensive in any way.

The book is very useful for introductory database management courses.

Reviewed by Adam Lee, Fellow, University of Maryland on 2/1/18

The book contains the Table of Contents and lists Key Terms for every Chapter. However, it will be better to include Index with corresponding page numbers and/or hyperlinks. read more

The book contains the Table of Contents and lists Key Terms for every Chapter. However, it will be better to include Index with corresponding page numbers and/or hyperlinks.

Content is accurate with many examples.

Better to add some more brief introduction on non-traditional databases and maybe some compact comparison table as well.

The figures and the tables are clear. Some itemized list may extend with longer explanations. Chapter 3 may be improved by putting some sections as sub-sections under other sections. Chapter 4 may explain the Physical Data Models as well. On relational model, the Primary Keys should be highlighted, e.g. bolded.

The whole book should be standardized with one ERD format. The early chapter uses the Chen's notation. The key chapter uses and explains the Crow's Feet notation. The appendix uses the UML.

The content includes all key components and topics about traditional database management systems.

Chapters 8, 9 and 10 may be re-arranged in sequence.

The pages are all good.

The grammar reads well.

The content reads neutral on cultural relevance.

Maybe CREATE VIEW and DROP VIEW can be added.

Reviewed by Benjamin Branch, Professor-LSIS, North Carolina Central University on 2/1/18

It give a sound history and need for database without picking one other the other. It also comes in several formats from pdf to Kindle and etc. It provide a clear and unbiased history of data effort and societal engagement with data. It give a... read more

It give a sound history and need for database without picking one other the other. It also comes in several formats from pdf to Kindle and etc. It provide a clear and unbiased history of data effort and societal engagement with data. It give a good scope of data base design. It does not include any open source database topics like Hadoop or MongoDB or their influx on the database market, learning or industry.

Such would have to be supplemented materials.

It accuracy is very good and it admits is covers most topics in databases.It's a great intro to databases book.

For a free book it is awesome. Even though cloud databases have existed since 1996, their use is now only becoming mainstream, but the basics of databases remains in tact.It does not include GIS or apps as extensions of databases in 2017.

For intro to database awesome, but not for an advanced databases course.

it has the properly communication for a novice database person getting into the field. The book give a great introduction to database and is clear throughout.

The book is written in a consistent manner and attempts not to lose the learner. The book uses some mathematical formula and needs more.

The book is broken down well into many chapters and digestible chunks of thought that are easily builds upon previous learning. the various chapters do a great job breaking down the database knowledge.

the book is very well organized and thought out. It give good examples for student to build up with homework to test their perceptions. The book sets up the learning of material and give a learner an opportunity to apply it in homework. Also, vocabulary is used to add to the database literacy of the student.

it was simple to use for an introductory course. Now real world experience would still need to be done in a hands-on manner. It gave examples and key terms to support more learning for another course. All graphics and layout were done very nicely for the online and pdf versions.

The grammar use was excellent. It made clear objectives and outcomes for learning.

not really applicable

A 3rd edition would be welcomed!!!

Reviewed by mary gable, instructor, tidewater community college on 8/15/17

The book covers all necessary areas and topics, but I did not see an overall index. I like the idea of having key terms at the end of each section. It was a great book for database design and as an extra bonus, SQL was covered in greater detail... read more

The book covers all necessary areas and topics, but I did not see an overall index. I like the idea of having key terms at the end of each section. It was a great book for database design and as an extra bonus, SQL was covered in greater detail than most texts on the subject. It is complete with section review, exercises, and solutions. Table of contents is complete, organized, and the topics are presented in the appropriate order.

Topics are described accurately and content is free of errors.

The topic here is traditional theory which does not change quickly as typical IT topics do. All content is relevant and up-to-date examples are used. Any updates should be easily implemented, but very few would be expected.

The book was very clear and topics were explained thoroughly. The terminology used was easy to understand. Key terms for each section were conveniently located at the end of each section which explained the important terms. This could easily be used as a section review.

The text, terminology, and terms are consistent throughout all sections of the book.

The book was consistent with all the typical modules and sections with key terms, questions, and lab exercises which reinforced concepts covered in each section. It is well-organized and reading flowed easily. Terms were presented in the appropriate order and the text was not verbose.

Topics are presented in a logical fashion. This organization is typical of all other database design courses I have seen.

Interface rating: 2

Many of the images are blurry and difficult to see. I had to zoom very large and they were still sometimes very hard to read. Navigation was typical of PDF documents and easy to move around and navigate throughout the document.

Grammatical Errors rating: 4

I found a few grammatical errors.

The text is not insensitive or offensive. Examples are typical business examples which are relevant and current. Example database designs are very simple to comprehend so that emphasis is placed on learning the concepts.

I think this book would make an excellent textbook for a relational database design course. It is complete with exercises and section reviews. The exercises are very beneficial and solutions to examples and labs are included with the text which is very important to the student. It would also be good to use for a SQL review.

Reviewed by Krishnendu Ghosh, Assistant Professor, Miami University on 6/20/17

The book is a thorough and covers the topics that is expected to be covered in a database design course. The topics are well organized. I was expecting material on NoSQL to be included and other recent updates in databases read more

The book is a thorough and covers the topics that is expected to be covered in a database design course. The topics are well organized. I was expecting material on NoSQL to be included and other recent updates in databases

The material in the book was accurate.

The book covers the material required in database design well and will be relevant in future. The recent advances in the database design should be incorporated.

The book is clear and concise.

The book is consistent in terms of terminology and organization of the concepts in every chapter.

The chapters in the book provide modularity. The instructor can select the topics accordingly.

The organization and structure is broad and comprehensive. I would hope there could have been more worked out examples.

The interface is great.

I did not find any errors in grammar

I did not find any culturally insensitive or offensive material.

This book is an ideal textbook for database design. The organization of the book helps in the understanding of the material at a low gradient. I hope the authors add more worked out examples.

Table of Contents

• Chapter 1 Before the Advent of Database Systems
• Chapter 2 Fundamental Concepts
• Chapter 3 Characteristics and Benefits of a Database
• Chapter 4 Types of Data Models
• Chapter 5 Data Modelling
• Chapter 6 Classification of Database Management Systems
• Chapter 7 The Relational Data Model
• Chapter 8 The Entity Relationship Data Model
• Chapter 9 Integrity Rules and Constraints
• Chapter 10 ER Modelling
• Chapter 11 Functional Dependencies
• Chapter 12 Normalization
• Chapter 13 Database Development Process
• Chapter 14 Database Users
• Chapter 15 SQL Structured Query Language
• Chapter 16 SQL Data Manipulation Language

Ancillary Material

About the book.

This second edition of Database Design book covers the concepts used in database systems and the database design process. Topics include:

• The history of databases
• Characteristics and benefits of databases
• Data models
• Data modelling
• Classification of database management systems
• Integrity rules and constraints
• Functional dependencies
• Normalization
• Database development process

New to this edition are more examples, highlighted and defined key terms, both throughout and at the end of each chapter, and end-of-chapter review exercises. Two new chapters have been added on SQL, along with appendices that include a data model example, sample ERD exercises and SQL lab with solutions.

About the Contributors

Adrienne Watt holds a computer systems diploma (BCIT), a bachelor’s degree in technology (BCIT) and a master’s degree in business administration (City University).

Since 1989, Adrienne has worked as an educator and gained extensive experience developing and delivering business and technology curriculum to post-secondary students. During that time, she ran a successful software development business. In the business, she worked as an IT professional in a variety of senior positions including project manager, database designer, administrator and business analyst. Recently she has been exploring a wide range of technology-related tools and processes to improve delivery methods and enhance learning for her students.

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Data management systems are the corner-stone of modern applications, businesses, and science (including data). If you were excited by the topics in 4111, this graduate level course in database systems research will be a deep dive into classic and modern database systems research. Topics will range from classic database system design, modern optimizations in single-machine and multi-machine settings, data cleaning and quality, and application-oriented databases. This semester’s theme will look at how learning has affected many classic data management systems challenges, and also how data management systems support and extends ML needs.

See FAQ for difference between 6113 and the other database courses.

• Class: Th 2-4PM in 829 Mudd
• Instructor: Eugene Wu , OH: Thurs 12-1PM 421 Mudd
• Syllabus & FAQ , Slack , Project , Papers
• Prereqs: W4111 Intro to DB (required), W4112 DB Implementations (recommended). Ugrads OK; see Prof Wu
• Discussion Prep 30%
• Class participation 30%
• Project 40%: Final Presentation 10% , Paper 30%

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Tentative schedule.

Course design inspired by

• Cal’s CS286
• Waterloo’s CS848
• Colin Raffel’s Role playing seminar
• Carl Vondrick’s self supervision graduate seminar

Database design basics

A properly designed database provides you with access to up-to-date, accurate information. Because a correct design is essential to achieving your goals in working with a database, investing the time required to learn the principles of good design makes sense. In the end, you are much more likely to end up with a database that meets your needs and can easily accommodate change.

This article provides guidelines for planning a desktop database. You will learn how to decide what information you need, how to divide that information into the appropriate tables and columns, and how those tables relate to each other. You should read this article before you create your first desktop database.

In this article

Some database terms to know, what is good database design, the design process, determining the purpose of your database, finding and organizing the required information, dividing the information into tables, turning information items into columns, specifying primary keys, creating the table relationships, refining the design, applying the normalization rules.

Access organizes your information into tables : lists of rows and columns reminiscent of an accountant’s pad or a spreadsheet. In a simple database, you might have only one table. For most databases you will need more than one. For example, you might have a table that stores information about products, another table that stores information about orders, and another table with information about customers.

Each row is more correctly called a record , and each column, a field . A record is a meaningful and consistent way to combine information about something. A field is a single item of information — an item type that appears in every record. In the Products table, for instance, each row or record would hold information about one product. Each column or field holds some type of information about that product, such as its name or price.

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Certain principles guide the database design process. The first principle is that duplicate information (also called redundant data) is bad, because it wastes space and increases the likelihood of errors and inconsistencies. The second principle is that the correctness and completeness of information is important. If your database contains incorrect information, any reports that pull information from the database will also contain incorrect information. As a result, any decisions you make that are based on those reports will then be misinformed.

A good database design is, therefore, one that:

Divides your information into subject-based tables to reduce redundant data.

Provides Access with the information it requires to join the information in the tables together as needed.

Helps support and ensure the accuracy and integrity of your information.

Accommodates your data processing and reporting needs.

The design process consists of the following steps:

Determine the purpose of your database     

This helps prepare you for the remaining steps.

Find and organize the information required     

Gather all of the types of information you might want to record in the database, such as product name and order number.

Divide the information into tables     

Divide your information items into major entities or subjects, such as Products or Orders. Each subject then becomes a table.

Turn information items into columns     

Decide what information you want to store in each table. Each item becomes a field, and is displayed as a column in the table. For example, an Employees table might include fields such as Last Name and Hire Date.

Specify primary keys     

Choose each table’s primary key. The primary key is a column that is used to uniquely identify each row. An example might be Product ID or Order ID.

Set up the table relationships     

Look at each table and decide how the data in one table is related to the data in other tables. Add fields to tables or create new tables to clarify the relationships, as necessary.

Refine your design     

Analyze your design for errors. Create the tables and add a few records of sample data. See if you can get the results you want from your tables. Make adjustments to the design, as needed.

Apply the normalization rules     

Apply the data normalization rules to see if your tables are structured correctly. Make adjustments to the tables, as needed.

It is a good idea to write down the purpose of the database on paper — its purpose, how you expect to use it, and who will use it. For a small database for a home based business, for example, you might write something simple like "The customer database keeps a list of customer information for the purpose of producing mailings and reports." If the database is more complex or is used by many people, as often occurs in a corporate setting, the purpose could easily be a paragraph or more and should include when and how each person will use the database. The idea is to have a well developed mission statement that can be referred to throughout the design process. Having such a statement helps you focus on your goals when you make decisions.

To find and organize the information required, start with your existing information. For example, you might record purchase orders in a ledger or keep customer information on paper forms in a file cabinet. Gather those documents and list each type of information shown (for example, each box that you fill in on a form). If you don't have any existing forms, imagine instead that you have to design a form to record the customer information. What information would you put on the form? What fill-in boxes would you create? Identify and list each of these items. For example, suppose you currently keep the customer list on index cards. Examining these cards might show that each card holds a customers name, address, city, state, postal code and telephone number. Each of these items represents a potential column in a table.

As you prepare this list, don’t worry about getting it perfect at first. Instead, list each item that comes to mind. If someone else will be using the database, ask for their ideas, too. You can fine-tune the list later.

Next, consider the types of reports or mailings you might want to produce from the database. For instance, you might want a product sales report to show sales by region, or an inventory summary report that shows product inventory levels. You might also want to generate form letters to send to customers that announces a sale event or offers a premium. Design the report in your mind, and imagine what it would look like. What information would you place on the report? List each item. Do the same for the form letter and for any other report you anticipate creating.

Giving thought to the reports and mailings you might want to create helps you identify items you will need in your database. For example, suppose you give customers the opportunity to opt in to (or out of) periodic e-mail updates, and you want to print a listing of those who have opted in. To record that information, you add a “Send e-mail” column to the customer table. For each customer, you can set the field to Yes or No.

The requirement to send e-mail messages to customers suggests another item to record. Once you know that a customer wants to receive e-mail messages, you will also need to know the e-mail address to which to send them. Therefore you need to record an e-mail address for each customer.

It makes good sense to construct a prototype of each report or output listing and consider what items you will need to produce the report. For instance, when you examine a form letter, a few things might come to mind. If you want to include a proper salutation — for example, the "Mr.", "Mrs." or "Ms." string that starts a greeting, you will have to create a salutation item. Also, you might typically start a letter with “Dear Mr. Smith”, rather than “Dear. Mr. Sylvester Smith”. This suggests you would typically want to store the last name separate from the first name.

A key point to remember is that you should break each piece of information into its smallest useful parts. In the case of a name, to make the last name readily available, you will break the name into two parts — First Name and Last Name. To sort a report by last name, for example, it helps to have the customer's last name stored separately. In general, if you want to sort, search, calculate, or report based on an item of information, you should put that item in its own field.

Think about the questions you might want the database to answer. For instance, how many sales of your featured product did you close last month? Where do your best customers live? Who is the supplier for your best-selling product? Anticipating these questions helps you zero in on additional items to record.

After gathering this information, you are ready for the next step.

To divide the information into tables, choose the major entities, or subjects. For example, after finding and organizing information for a product sales database, the preliminary list might look like this:

The major entities shown here are the products, the suppliers, the customers, and the orders. Therefore, it makes sense to start out with these four tables: one for facts about products, one for facts about suppliers, one for facts about customers, and one for facts about orders. Although this doesn’t complete the list, it is a good starting point. You can continue to refine this list until you have a design that works well.

When you first review the preliminary list of items, you might be tempted to place them all in a single table, instead of the four shown in the preceding illustration. You will learn here why that is a bad idea. Consider for a moment, the table shown here:

In this case, each row contains information about both the product and its supplier. Because you can have many products from the same supplier, the supplier name and address information has to be repeated many times. This wastes disk space. Recording the supplier information only once in a separate Suppliers table, and then linking that table to the Products table, is a much better solution.

A second problem with this design comes about when you need to modify information about the supplier. For example, suppose you need to change a supplier's address. Because it appears in many places, you might accidentally change the address in one place but forget to change it in the others. Recording the supplier’s address in only one place solves the problem.

When you design your database, always try to record each fact just once. If you find yourself repeating the same information in more than one place, such as the address for a particular supplier, place that information in a separate table.

Finally, suppose there is only one product supplied by Coho Winery, and you want to delete the product, but retain the supplier name and address information. How would you delete the product record without also losing the supplier information? You can't. Because each record contains facts about a product, as well as facts about a supplier, you cannot delete one without deleting the other. To keep these facts separate, you must split the one table into two: one table for product information, and another table for supplier information. Deleting a product record should delete only the facts about the product, not the facts about the supplier.

Once you have chosen the subject that is represented by a table, columns in that table should store facts only about the subject. For instance, the product table should store facts only about products. Because the supplier address is a fact about the supplier, and not a fact about the product, it belongs in the supplier table.

To determine the columns in a table, decide what information you need to track about the subject recorded in the table. For example, for the Customers table, Name, Address, City-State-Zip, Send e-mail, Salutation and E-mail address comprise a good starting list of columns. Each record in the table contains the same set of columns, so you can store Name, Address, City-State-Zip, Send e-mail, Salutation and E-mail address information for each record. For example, the address column contains customers’ addresses. Each record contains data about one customer, and the address field contains the address for that customer.

Once you have determined the initial set of columns for each table, you can further refine the columns. For example, it makes sense to store the customer name as two separate columns: first name and last name, so that you can sort, search, and index on just those columns. Similarly, the address actually consists of five separate components, address, city, state, postal code, and country/region, and it also makes sense to store them in separate columns. If you want to perform a search, filter or sort operation by state, for example, you need the state information stored in a separate column.

You should also consider whether the database will hold information that is of domestic origin only, or international, as well. For instance, if you plan to store international addresses, it is better to have a Region column instead of State, because such a column can accommodate both domestic states and the regions of other countries/regions. Similarly, Postal Code makes more sense than Zip Code if you are going to store international addresses.

The following list shows a few tips for determining your columns.

Don’t include calculated data     

In most cases, you should not store the result of calculations in tables. Instead, you can have Access perform the calculations when you want to see the result. For example, suppose there is a Products On Order report that displays the subtotal of units on order for each category of product in the database. However, there is no Units On Order subtotal column in any table. Instead, the Products table includes a Units On Order column that stores the units on order for each product. Using that data, Access calculates the subtotal each time you print the report. The subtotal itself should not be stored in a table.

Store information in its smallest logical parts     

You may be tempted to have a single field for full names, or for product names along with product descriptions. If you combine more than one kind of information in a field, it is difficult to retrieve individual facts later. Try to break down information into logical parts; for example, create separate fields for first and last name, or for product name, category, and description.

Once you have refined the data columns in each table, you are ready to choose each table's primary key.

Each table should include a column or set of columns that uniquely identifies each row stored in the table. This is often a unique identification number, such as an employee ID number or a serial number. In database terminology, this information is called the primary key of the table. Access uses primary key fields to quickly associate data from multiple tables and bring the data together for you.

If you already have a unique identifier for a table, such as a product number that uniquely identifies each product in your catalog, you can use that identifier as the table’s primary key — but only if the values in this column will always be different for each record. You cannot have duplicate values in a primary key. For example, don’t use people’s names as a primary key, because names are not unique. You could easily have two people with the same name in the same table.

A primary key must always have a value. If a column's value can become unassigned or unknown (a missing value) at some point, it can't be used as a component in a primary key.

You should always choose a primary key whose value will not change. In a database that uses more than one table, a table’s primary key can be used as a reference in other tables. If the primary key changes, the change must also be applied everywhere the key is referenced. Using a primary key that will not change reduces the chance that the primary key might become out of sync with other tables that reference it.

Often, an arbitrary unique number is used as the primary key. For example, you might assign each order a unique order number. The order number's only purpose is to identify an order. Once assigned, it never changes.

If you don’t have in mind a column or set of columns that might make a good primary key, consider using a column that has the AutoNumber data type. When you use the AutoNumber data type, Access automatically assigns a value for you. Such an identifier is factless; it contains no factual information describing the row that it represents. Factless identifiers are ideal for use as a primary key because they do not change. A primary key that contains facts about a row — a telephone number or a customer name, for example — is more likely to change, because the factual information itself might change.

1. A column set to the AutoNumber data type often makes a good primary key. No two product IDs are the same.

In some cases, you may want to use two or more fields that, together, provide the primary key of a table. For example, an Order Details table that stores line items for orders would use two columns in its primary key: Order ID and Product ID. When a primary key employs more than one column, it is also called a composite key.

For the product sales database, you can create an AutoNumber column for each of the tables to serve as primary key: ProductID for the Products table, OrderID for the Orders table, CustomerID for the Customers table, and SupplierID for the Suppliers table.

Now that you have divided your information into tables, you need a way to bring the information together again in meaningful ways. For example, the following form includes information from several tables.

1. Information in this form comes from the Customers table...

2. ...the Employees table...

3. ...the Orders table...

4. ...the Products table...

5. ...and the Order Details table.

Access is a relational database management system. In a relational database, you divide your information into separate, subject-based tables. You then use table relationships to bring the information together as needed.

Creating a one-to-many relationship

Consider this example: the Suppliers and Products tables in the product orders database. A supplier can supply any number of products. It follows that for any supplier represented in the Suppliers table, there can be many products represented in the Products table. The relationship between the Suppliers table and the Products table is, therefore, a one-to-many relationship.

To represent a one-to-many relationship in your database design, take the primary key on the "one" side of the relationship and add it as an additional column or columns to the table on the "many" side of the relationship. In this case, for example, you add the Supplier ID column from the Suppliers table to the Products table. Access can then use the supplier ID number in the Products table to locate the correct supplier for each product.

The Supplier ID column in the Products table is called a foreign key. A foreign key is another table’s primary key. The Supplier ID column in the Products table is a foreign key because it is also the primary key in the Suppliers table.

You provide the basis for joining related tables by establishing pairings of primary keys and foreign keys. If you are not sure which tables should share a common column, identifying a one-to-many relationship ensures that the two tables involved will, indeed, require a shared column.

Creating a many-to-many relationship

Consider the relationship between the Products table and Orders table.

A single order can include more than one product. On the other hand, a single product can appear on many orders. Therefore, for each record in the Orders table, there can be many records in the Products table. And for each record in the Products table, there can be many records in the Orders table. This type of relationship is called a many-to-many relationship because for any product, there can be many orders; and for any order, there can be many products. Note that to detect many-to-many relationships between your tables, it is important that you consider both sides of the relationship.

The subjects of the two tables — orders and products — have a many-to-many relationship. This presents a problem. To understand the problem, imagine what would happen if you tried to create the relationship between the two tables by adding the Product ID field to the Orders table. To have more than one product per order, you need more than one record in the Orders table per order. You would be repeating order information for each row that relates to a single order — resulting in an inefficient design that could lead to inaccurate data. You run into the same problem if you put the Order ID field in the Products table — you would have more than one record in the Products table for each product. How do you solve this problem?

The answer is to create a third table, often called a junction table, that breaks down the many-to-many relationship into two one-to-many relationships. You insert the primary key from each of the two tables into the third table. As a result, the third table records each occurrence or instance of the relationship.

Each record in the Order Details table represents one line item on an order. The Order Details table’s primary key consists of two fields — the foreign keys from the Orders and the Products tables. Using the Order ID field alone doesn’t work as the primary key for this table, because one order can have many line items. The Order ID is repeated for each line item on an order, so the field doesn’t contain unique values. Using the Product ID field alone doesn’t work either, because one product can appear on many different orders. But together, the two fields always produce a unique value for each record.

In the product sales database, the Orders table and the Products table are not related to each other directly. Instead, they are related indirectly through the Order Details table. The many-to-many relationship between orders and products is represented in the database by using two one-to-many relationships:

The Orders table and Order Details table have a one-to-many relationship. Each order can have more than one line item, but each line item is connected to only one order.

The Products table and Order Details table have a one-to-many relationship. Each product can have many line items associated with it, but each line item refers to only one product.

From the Order Details table, you can determine all of the products on a particular order. You can also determine all of the orders for a particular product.

After incorporating the Order Details table, the list of tables and fields might look something like this:

Creating a one-to-one relationship

Another type of relationship is the one-to-one relationship. For instance, suppose you need to record some special supplementary product information that you will need rarely or that only applies to a few products. Because you don't need the information often, and because storing the information in the Products table would result in empty space for every product to which it doesn’t apply, you place it in a separate table. Like the Products table, you use the ProductID as the primary key. The relationship between this supplemental table and the Product table is a one-to-one relationship. For each record in the Product table, there exists a single matching record in the supplemental table. When you do identify such a relationship, both tables must share a common field.

When you detect the need for a one-to-one relationship in your database, consider whether you can put the information from the two tables together in one table. If you don’t want to do that for some reason, perhaps because it would result in a lot of empty space, the following list shows how you would represent the relationship in your design:

If the two tables have the same subject, you can probably set up the relationship by using the same primary key in both tables.

If the two tables have different subjects with different primary keys, choose one of the tables (either one) and insert its primary key in the other table as a foreign key.

Determining the relationships between tables helps you ensure that you have the right tables and columns. When a one-to-one or one-to-many relationship exists, the tables involved need to share a common column or columns. When a many-to-many relationship exists, a third table is needed to represent the relationship.

Once you have the tables, fields, and relationships you need, you should create and populate your tables with sample data and try working with the information: creating queries, adding new records, and so on. Doing this helps highlight potential problems — for example, you might need to add a column that you forgot to insert during your design phase, or you may have a table that you should split into two tables to remove duplication.

See if you can use the database to get the answers you want. Create rough drafts of your forms and reports and see if they show the data you expect. Look for unnecessary duplication of data and, when you find any, alter your design to eliminate it.

As you try out your initial database, you will probably discover room for improvement. Here are a few things to check for:

Did you forget any columns? If so, does the information belong in the existing tables? If it is information about something else, you may need to create another table. Create a column for every information item you need to track. If the information can’t be calculated from other columns, it is likely that you will need a new column for it.

Are any columns unnecessary because they can be calculated from existing fields? If an information item can be calculated from other existing columns — a discounted price calculated from the retail price, for example — it is usually better to do just that, and avoid creating new column.

Are you repeatedly entering duplicate information in one of your tables? If so, you probably need to divide the table into two tables that have a one-to-many relationship.

Do you have tables with many fields, a limited number of records, and many empty fields in individual records? If so, think about redesigning the table so it has fewer fields and more records.

Has each information item been broken into its smallest useful parts? If you need to report, sort, search, or calculate on an item of information, put that item in its own column.

Does each column contain a fact about the table's subject? If a column does not contain information about the table's subject, it belongs in a different table.

Are all relationships between tables represented, either by common fields or by a third table? One-to-one and one-to- many relationships require common columns. Many-to-many relationships require a third table.

Refining the Products table

Suppose that each product in the product sales database falls under a general category, such as beverages, condiments, or seafood. The Products table could include a field that shows the category of each product.

Suppose that after examining and refining the design of the database, you decide to store a description of the category along with its name. If you add a Category Description field to the Products table, you have to repeat each category description for each product that falls under the category — this is not a good solution.

A better solution is to make Categories a new subject for the database to track, with its own table and its own primary key. You can then add the primary key from the Categories table to the Products table as a foreign key.

The Categories and Products tables have a one-to-many relationship: a category can include more than one product, but a product can belong to only one category.

When you review your table structures, be on the lookout for repeating groups. For example, consider a table containing the following columns:

Product ID1

Product ID2

Product ID3

Here, each product is a repeating group of columns that differs from the others only by adding a number to the end of the column name. When you see columns numbered this way, you should revisit your design.

Such a design has several flaws. For starters, it forces you to place an upper limit on the number of products. As soon as you exceed that limit, you must add a new group of columns to the table structure, which is a major administrative task.

Another problem is that those suppliers that have fewer than the maximum number of products will waste some space, since the additional columns will be blank. The most serious flaw with such a design is that it makes many tasks difficult to perform, such as sorting or indexing the table by product ID or name.

Whenever you see repeating groups review the design closely with an eye on splitting the table in two. In the above example it is better to use two tables, one for suppliers and one for products, linked by supplier ID.

You can apply the data normalization rules (sometimes just called normalization rules) as the next step in your design. You use these rules to see if your tables are structured correctly. The process of applying the rules to your database design is called normalizing the database, or just normalization.

Normalization is most useful after you have represented all of the information items and have arrived at a preliminary design. The idea is to help you ensure that you have divided your information items into the appropriate tables. What normalization cannot do is ensure that you have all the correct data items to begin with.

You apply the rules in succession, at each step ensuring that your design arrives at one of what is known as the "normal forms." Five normal forms are widely accepted — the first normal form through the fifth normal form. This article expands on the first three, because they are all that is required for the majority of database designs.

First normal form

First normal form states that at every row and column intersection in the table there, exists a single value, and never a list of values. For example, you cannot have a field named Price in which you place more than one Price. If you think of each intersection of rows and columns as a cell, each cell can hold only one value.

Second normal form

Second normal form requires that each non-key column be fully dependent on the entire primary key, not on just part of the key. This rule applies when you have a primary key that consists of more than one column. For example, suppose you have a table containing the following columns, where Order ID and Product ID form the primary key:

Order ID (primary key)

Product ID (primary key)

Product Name

This design violates second normal form, because Product Name is dependent on Product ID, but not on Order ID, so it is not dependent on the entire primary key. You must remove Product Name from the table. It belongs in a different table (Products).

Third normal form

Third normal form requires that not only every non-key column be dependent on the entire primary key, but that non-key columns be independent of each other.

Another way of saying this is that each non-key column must be dependent on the primary key and nothing but the primary key. For example, suppose you have a table containing the following columns:

ProductID (primary key)

Assume that Discount depends on the suggested retail price (SRP). This table violates third normal form because a non-key column, Discount, depends on another non-key column, SRP. Column independence means that you should be able to change any non-key column without affecting any other column. If you change a value in the SRP field, the Discount would change accordingly, thus violating that rule. In this case Discount should be moved to another table that is keyed on SRP.

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Online Programming Lessons, Tutorials and Capstone Project guide

40 List of DBMS Project Topics and Ideas

Introduction

A Capstone project is the last project of an IT degree program. It is made up of one or more research projects in which students create prototypes, services, and/or products. The projects are organized around an issue that needs to be handled in real-world scenarios. When IT departments want to test new ideas or concepts that will be adopted into their daily operations, they implement these capstone projects within their services.

In this article, our team has compiled a list of Database Management System Project Topics and Ideas. The capstone projects listed below will assist future researchers in deciding which capstone project idea to pursue. Future researchers may find the information in this page useful in coming up with unique capstone project ideas.

• Telemedicine Online Platform Database Design

  “Telemedicine Online Platform” is designed to allow doctors to deliver clinical support to patients remotely. Doctors can communicate with their patients in real-time for consultations, diagnoses, monitoring, and medical supply prescriptions. The project will be developed using the SDLC method by the researchers. The researchers will also compile a sample of hospital doctors and patients who will act as study participants. A panel of IT specialists will review, test, and assess the project.

• Virtual and Remote Guidance Counselling System Database Design

Counseling is a vital component of a person’s life since it aids in the improvement of interpersonal relationships. Humans must cease ignoring this issue because it is essential for the development of mental wellness. The capstone project “Virtual and Remote Guidance Counselling System,” which covers the gap in giving counseling in stressful situations, was built for this reason. It answers to the requirement to fill in the gaps in the traditional technique and make it more effective and immersive in this way.

• COVID-19 Facilities Management Information System Database Design

COVID – 19 has put people in fear due to its capability of transmission when exposed to the virus. The health sectors and the government provide isolation facilities for COVID-19 patients to mitigate the spread and transmission of the virus. However, proper communication for the availability of the facilities is inefficient resulting to surge of patients in just one facility and some are transferred multiple times due to unavailability. The COVID-19 respondents must have an advance tools to manage the COVID-19 facilities where respondents can easily look for available facilities to cater more patients.

• Document Tracking System Database Design

The capstone project, “Document Tracking System” is purposely designed for companies and organizations that allow them to electronically store and track documents. The system will track the in/out of the documents across different departments. The typical way of tracking documents is done using the manual approach. The staff will call or personally ask for updates about the documents which are time-consuming and inefficient.

• Face Recognition Application Database Design

Technology has grown so fast; it changes the way we do our daily tasks. Technology has made our daily lives easier. The capstone project, entitled “Face Recognition Attendance System” is designed to automate checking and recording of students’ attendance during school events using face recognition technology. The system will work by storing the student’s information along with their photographs in a server and the system will detect the faces of the students during school events and match it and verify to record the presence or absence of the student.

• Digital Wallet Solution Database Design

The capstone project, named “Digital Wallet Solution,” is intended to allow people to store money online and make payments online. The digital wallet transactions accept a variety of currencies and provide a variety of payment gateways via which the user can pay for products and services. The system allows users to conduct secure and convenient online financial transactions. It will speed up payment and other financial processes, reducing the amount of time and effort required to complete them.

• Virtual Online Tour Application Database Design

The usage of technology is an advantage in the business industry, especially during this challenging pandemic. It allows businesses to continue to operate beyond physicality. The capstone project entitled “Virtual Online Tour Application” is designed as a platform to streamline virtual tours for clients. Any business industry can use the system to accommodate and provide their clients with a virtual experience of their business. For example, the tourist industry and real estate agencies can use the system to provide a virtual tour to their clients about the tourist locations and designs of properties, respectively.

• Invoice Management System Database Design

The researchers will create a system that will make it easier for companies to manage and keep track of their invoice information. The company’s sales records, payables, and total invoice records will all be electronically managed using this project. Technology is highly used for business operations and transactions automation. The capstone project, entitled “Invoice Management System” is designed to automate the management of the company’s invoice records. The said project will help companies to have an organized, accurate, and reliable record that will help them track their sales and finances.

• Vehicle Repair and Maintenance Management System Database Design

Information Technology has become an integral part of any kind of business in terms of automating business operations and transactions. The capstone project, entitled “Vehicle Repair and Maintenance Management System” is designed for vehicle repair and maintenance management automation. The said project will automate the vehicle garage’s operations and daily transactions. The system will automate operations such as managing vehicle repair and maintenance records, invoice records, customer records, transaction records, billing and payment records, and transaction records.

• Transcribe Medical Database Design

Information technology has made everything easier and simpler, including transcribing the medical diagnosis of patients. The capstone project, entitled “Medical Transcription Platform,” is designed to allow medical transcriptionists to transcribe audio of medical consultations and diagnose patients in a centralized manner. A medical transcriptionist is vital to keep accurate and credible medical records of patients and can be used by other doctors to know the patients’ medical history. The said project will serve as a platform where transcribed medical audios are stored for safekeeping and easy retrieval.

• Multi-branch Travel Agency and Booking System Database Design

The capstone project, entitled “Multi-Branch Travel Agency and Booking System,” is designed as a centralized platform wherein multiple travel agency branches are registered to ease and simplify inquiries and booking of travels and tour packages by clients. The said project will allow travel agencies to operate a business in an easy, fast manner considering the convenience and safety of their clients. The system will enable travel agencies and their clients to have a seamless online transaction.

• Pharmacy Stocks Management Database Design

The capstone project “Pharmacy Stocks Management System” allows pharmacies to manage and monitor their stocks of drugs electronically. The Pharmacy Stocks Management System will automate inventory to help ensure that the pharmacy has enough stock of medications and supplies to serve the needs of the patients.

• Loan Management with SMS Database Design

The capstone project entitled “ Loan Management System with SMS ” is an online platform that allows members to apply and request loan. In addition, they can also monitor their balance in their respective dashboard. Management of cooperative will review first the application for approval or disapproval of the request. Notification will be send through the SMS or short messaging service feature of the system.

• Service Call Management System Database Design

The capstone project, entitled ” Service Call Management System,” is designed to transform service calls to a centralized platform. The said project would allow clients to log in and lodge calls to the tech support if they encountered issues and difficulties with their purchased products. The tech support team will diagnose the issue and provide them with the necessary actions to perform via a call to solve the problem and achieve satisfaction.

• File Management with Approval Process Database Design

The File Management System provides a platform for submitting, approving, storing, and retrieving files. Specifically, the capstone project is for the file management of various business organizations. This is quite beneficial in the management and organization of the files of every department. Installation of the system on an intranet is possible, as is uploading the system to a live server, from which the platform can be viewed online and through the use of a browser.

• Beauty Parlor Management System Database Design

The capstone project entitled “Beauty Parlour Management System” is an example of transactional processing system that focuses on the records and process of a beauty parlour. This online application will help the management to keep and manage their transactions in an organize, fast and efficient manner.

• Exam Management System Database Design

Information technology plays a significant role in the teaching and learning process of teachers and students, respectively. IT offers a more efficient and convenient way for teachers and students to learn and assess learnings. The capstone project, “Exam Management System,” is designed to allow electronic management of all the information about the exam questions, courses and subjects, and teachers and students. The said project is an all-in-one platform for student exam management.

• Student and Faculty Clearance Database Design

The capstone project, entitled “Student and Faculty Clearance System,” is designed to automate students and faculty clearance processes. The approach is intended to make the clearance procedure easier while also guaranteeing that approvals are accurate and complete. The project works by giving every Department involved access to the application. The proposed scheme can eliminate the specified challenges, streamline the process, and verify the integrity and correctness of the data.

• Vehicle Parking Management System Database Design

The capstone project entitled “ Vehicle Parking Management System ” is an online platform that allows vehicle owners to request or reserve a slot for parking space. Management can accept and decline the request of reservation. In addition, payment option is also part of the system feature but is limited to on-site payment.

• Hospital Resources and Room Utilization Database Design

The capstone project, “Hospital Resources and Room Utilization Management System” is a system designed to streamline the process of managing hospital resources and room utilization. The said project is critical especially now that we are facing a pandemic, there is a need for efficient management of hospital resources and room management. The management efficiency will prevent a shortage in supplies and overcrowding of patients in the hospitals.

• Church Event Management System Database Design

The capstone project entitled “Church Event Management System” is designed to be used by church organizations in creating and managing different church events. The conventional method of managing church events is done manually where members of organizations will face difficulties due to physical barriers and time constraints.

• CrowdFunding Platform Database Design

Business financing is critical for new business ventures. In this study, the researchers concentrate on designing and developing a business financing platform that is effective for new startups. This capstone project, entitled “Crowdfunding Platform” is a website that allows entrepreneurs to campaign their new business venture to attract investors and crowdfund.

• Vehicle Franchising and Drivers Offense Software Database Design

The proposed software will be used to electronically process and manage vehicle and franchising and driver’s offenses. The proposed software will eliminate the manual method which involves a lot of paper works and consumes valuable amount of time. The proposed project will serve as a centralized platform was recording and paying for the offenses committed by the drivers will be processed. The system will quicken the process of completing transaction between the enforcers and the drivers. Vehicle franchising and managing driver offenses will be easy, fast and convenient using the system.

• Student Tracking Performance Database Design

The capstone project entitled “Student Academic Performance Tracking and Monitoring System” allows academic institutions to monitor and gather data about the academic performance of students where decisions are derived to further improve the students learning outcomes. Tracking and monitoring student’s performance serves a vital role in providing information that is used to assist students, teachers, administrators, and policymakers in making decisions that will further improve the academic performance of students.

• Webinar Course Management System Database Design

The capstone project, entitled “Webinar Course Management System,” is designed to automate managing webinar courses. The project aims to eliminate the current method, which is inefficient and inconvenient for parties involved in the webinar. A software development life cycle (SDLC) technique will be used by the researchers in order to build this project. They will gather a sample size of participating webinar members and facilitators to serve as respondents of the study.

• Online Birth Certificate Processing System with SMS Notification Database Design

The capstone project, “Online Birth Certificate Processing System with SMS Notification “ is an IT-based solution that aims to automate the process of requesting, verifying, and approving inquiries for original birth records. The system will eliminate the traditional method and transition the birth certificate processing into an easy, convenient, and efficient manner. The researchers will develop the project following the Software Development Life Cycle (SDLC) technique.

• Food Donation Services Database Design

Information technology plays a significant role in automating the operations of many companies to boost efficiency. One of these is the automation of food donation and distribution management. “Food Donation Services,” the capstone project, is intended to serve as a platform for facilitating transactions between food groups, donors, and recipients. Food banks will be able to respond to various food donations and food assistance requests in a timely and effective manner as a result of the project.

• COVID Profiling Database Design

The capstone project “City COVID-19 Profiling System with Decision Support” is designed to automate the process of profiling COVID-19 patients. The project will empower local health officers in electronically recording and managing COVID-19 patient information such as symptoms, travel history, and other critical details needed to identify patients. Manual profiling is prone to human mistakes, necessitates a lot of paperwork, and needs too much time and effort from the employees.

• Evacuation Center Database Design

Calamities can have a significant impact on society. It may result in an enormous number of people being evacuated. The local government unit assigned evacuation centers to provide temporary shelter for people during disasters. Evacuation centers are provided to give temporary shelter for the people during and after a calamity. Evacuation centers can be churches, sports stadium community centers, and much more that are capable to provide emergency shelter.

• QR Code Fare Payment System Database Design

The capstone project, “QR Code Fare Payment System” is designed to automate the procedure of paying for a fare when riding a vehicle. Passengers will register in the system to receive their own QR code, which they will use to pay for their fares by scanning in the system’s QR code scanning page. The project will enable cashless fare payment.

• Web Based Psychopathology Diagnosis System Database Design

The capstone project entitled “Web-Based Psychopathology Diagnosis System” is designed for patients and medical staff in the field of psychopathology. The system will be a centralized platform to be used by patients and psychopathologists for consultations. The said project will also keep all the records electronically. Mental health is important. Each individual must give importance to their mental health by paying attention to it and seek medical advice if symptoms of mental disorders and unusual behavior occur.

• Service Marketplace System Database Design

The capstone project, “Services Marketplace System” is designed to serve as a centralized platform for marketing and inquiring about different services. The system will serve as a platform where different service providers and customers will have an automated transaction. Technology made it easier for people to accomplish daily tasks and activities. In the conventional method, customers avail themselves of services by visiting the shop that offers their desired services personally.

• Fish Catch System Database Design

The capstone project, entitled “Fish Catch Monitoring System” will automate the process of recording and monitoring fish catches. The said project is intended to be used by fisherman and fish markets to accurately record fish catches and will also keep the records electronically safe and secure.

• Complaints Handling Management System Free Template Database Design

The capstone project, “Complaint Handling Management System” is a system designed to help educational institutions to handle and manage complaints electronically. The system will improve the response time of the school’s management in addressing the complaints of the students, parents, staff, and other stakeholders.

• Senior Citizen Information System Free Template Database Design

The system will replace the manual method of managing information and records of the senior citizen to an electronic one. The system will serve as a repository of the record of the senior citizen within the scope of a specific local government unit. By using the system, paper works will be lessened and human errors in file handling will be avoided. The system is efficient enough to aid in managing and keeping the records of the senior citizens in the different barangay.

• Online and SMS-Based Salary Notification Database Design

The “Online and SMS Based Salary Notification” is a capstone project intended to be used by companies and employees to automate the process of notifying salary details. The application will work by allowing the designated company encoder to encode details of salary and the employees to log in to his/her account in the application and have access to the details of his/her salary. One of the beauties of being employed is being paid. Employers manage the employee’s salary and are responsible to discuss with the employees the system of the salary and deductions.

• Maternal Records Management Database Design

The capstone project, “Maternal Records Management System” is a system that automates the process of recording and keeping maternal records. The said project will allow maternity clinics to track and monitor their patients’ records from pregnancy to their baby’s immunization records.

• Online Complaint Management System Database Design

Online Complaint Management System is a capstone project that is design to serve as a platform to address complaints and resolve disputes. The system provides an online way of resolving problems faced by the public or people within the organization. The system will make complaints easier to coordinate, monitor, track and resolve.

• Online Donation Database Design

The capstone project ,  “Online Donation Platform for DSWD” is an online platform for giving and asking donations in the Department of Social Welfare and Development (DSWD). The system will be managed by the staffs of the DSWD to verify donors and legible beneficiaries electronically. The system will have an SMS feature to notify the donors and beneficiaries about the status of their request.

• OJT Timesheet Monitoring System using QR Code Database Design

The capstone project, “OJT Timesheet Monitoring System using QR Code” allows employer to automate timesheet of each trainee for easy monitoring. The system will be used by the on-the-job trainees to serve as their daily time in and out using the QR code generated by the system. The entire system will be managed by the administrator.

Technology is attributed with driving change in a wide range of enterprises and institutions. Because of information technology, the world has altered dramatically. It is difficult to imagine an industry or organization that has not benefited from technology advances. In these businesses, the most common role of IT has been to automate numerous procedures and transactions in order to increase efficiency and improve people’s overall experience and satisfaction. The aforementioned capstone project ideas will be useful in a range of sectors. It will aid in enhancing operational efficiency as well as the services provided to the project’s users.

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Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

Are you also learning about dynamic equilibrium in your science class? We break this sometimes tricky concept down so it's easy to understand in our complete guide to dynamic equilibrium .

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Top 18 database projects ideas for students.

In the realm of computer science and IT courses, a well-constructed database project can be a game-changer for students eager to showcase their skills. If you’re a student on the hunt for compelling database project ideas for your academic endeavors, you’re in the right place. From designing intuitive library management systems to creating dynamic student portals, the world of databases offers a plethora of opportunities. In this guide, we’ll delve into a variety of database project ideas specifically curated to spark inspiration and set you on the path to academic excellence.”

1. Inventory control management Database Project

Design goals: maintain a proper variety of required items, increase inventory turnover, reduce and maintain optimize inventory and safety stock levels, obtain low raw material prices, reduce storage cost, reduce insurance cost, reduce taxes.

Tables : Items, Inventory, InventoryTurnover, Suppliers, PurchaseOrders, OrderDetails, Storage, StorageCosts, Insurance, Taxes, SafetyStock, RawMaterials, RawMaterialPrices, Transactions, Vendors.

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2. Student Record Keeping System Database Project

Design goals: a student file that contains the information about the student, a stream file, a marks file, a fee file, concession/scholarship, etc.

Tables: Students, Streams, Marks, Fees, Scholarships, Concessions.

3. Online Retail Application Database Project

A customer can register to purchase an item. The customer will provide the bank account number and bank name (can have multiple account numbers). After registration, each customer will have a unique customer, user id, and password. A customer can purchase one or more items in different quantities. The items can of different classes based on their prices.

Based on the quantity, the price of the item, and discount (if any) on the purchased items, the bill will be generated. A bank account is required to settle the bill. The items can be ordered from one or more suppliers.

Tables: Customers, BankAccounts, Items, ItemClasses, Orders, OrderDetails, Discounts, Bills, Suppliers, SupplierItems.

4. College Database Project

A college contains many departments. Each department can offer any number of courses. Many instructors can work in a department, but an instructor can work only in one department. For each department, there is an ahead, and an instructor can be the head of only one department. Each instructor can take any number of courses, and a course can be taken by only one instructor.

A student can enroll in any number of courses and each course can have any number of students.

Tables: Departments, Courses, Instructors, DepartmentHeads, Enrollments, Students.

5. Railway System Database Project

A railway system, which needs to model the following:

• Tracks, connecting stations. You can assume for simplicity that only one track exists between any two stations. All the tracks put together to form a graph.
• Trains, with an ID and a name.
• Train schedules recording what time a train passes through each station on its route.

You can assume for simplicity that each train reaches its destination on the same day and that every train runs every day. Also, for simplicity, assume that for each train, for each station on its route, you store.

•  Time in.
•  Timeout (same as time in if it does not stop).
•  A sequence number so the stations in the route of a train can be ordered by sequence number.
• Passenger booking consisting of train, date, from-station, to station, coach, seat and passenger name.

Tables: Stations, Tracks, Trains, TrainSchedules, Coaches, Bookings, Passengers.

6. Hospital Management System Database Project

A patient will have a unique Patient ID. Full description about the patient about personal detail and phone number, and then Disease and what treatment is going on. The doctor will handle patients, one doctor can Treat more than 1 patient. Also, each doctor will have a unique ID. Doctor and Patients will be related. Patients can be admitted to the hospital.

So different room numbers will be there, also rooms for Operation Theaters and ICU.  There are some nurses, and ward boys for the maintenance of the hospital and for patient take care.  Based upon the number of days and treatment bill will be generated.

Tables: Patients, Doctors, Treatments, Rooms, OperationTheaters, ICUs, Nurses, WardBoys, Bills.

Check Hospital Management System Project

7. Library Management System Database Project

A student and faculty can issue books. Different limits for the number of books a student and teacher can issue. Also, the number of days will be distinct in the case of students and teachers for issue any book.  Each book will have a different ID. Also, each book of the same name and same author (but the number of copies) will have a different ID.

Entry of all the book will be done, who issue that book and when and also duration. Detail of Fine (when the book is not returned at a time) is also stored.

Tables: Students, Faculty, Books, Authors, BookIssues, Fines.

You can also check these posts:

• Blood Bank Management System
• Hotel Management System
• Payroll Management System
• Patient Information Management System 

8. Payroll Management System Database Project

There will entry (Unique ID) of all the employees of any organization. According to the date of joining and the date up to which salary is created, the number of days will be entered.  Basic pay will be defined according to the post of employee and department. Then component like DA, HRA, medical allowance, Arrears will be added, and Charges of Hostel/Bus, Security, welfare fund and other will be deducted. The number of leaves taken by the employee.

Tables: Employees, JoiningDetails, Departments, Posts, BasicPay, Allowances, Deductions, Leaves, Salaries.

9. Healthcare organization Database Project

This organization provides the following functionalities:

• Emergency Care 24×7
• Support Groups
• Support and Help Through calls

Any new Patient is first registered in their database before meeting the doctor. The Doctor can update the data related to the patient upon diagnosis (Including the disease diagnosed and prescription). This organization also provides rooms facility for admitting the patient who is critical. Apart from doctors, this organization has nurses and ward boys.

Each nurse and ward boy is assigned to a doctor. Also, they can be assigned to patients (to take care of them). The bill is paid by the patient with cash and E-banking. The record of each payment made is also maintained by the organization. The record of each call received to provide help and support to its existing person is also maintained.

Tables: Patients, Doctors, Diagnoses, Rooms, Nurses, WardBoys, Assignments, Payments, EmergencyCare, SupportGroups, SupportCalls.

Check Clinic Management System Project

10. Restaurant Management Database Project

• The restaurant maintains the catalog for the list of food and beverage items that it provides.
• Apart from providing food facilities at their own premises, the restaurant takes orders online through their site. Orders on the phone are also entertained.
• To deliver the orders, we have delivery boys. Each delivery boy is assigned to a specific area code. The delivery boy cannot deliver outside the area which is not assigned to the delivery boy (for every delivery boy there can be a single area assigned to that delivery boy).
• The customer record is maintained so that premium customers can be awarded discounts.

Tables: MenuItems, OnlineOrders, PhoneOrders, DeliveryBoys, AreaCodes, Customers, Discounts.

11. Design a Scenario and An Er Diagram for An It Training Group Database Project

It will meet the information needs of its training program. Clearly indicate the entities, relationships, and key constraints.

The description of the environment is as follows:

• The company has 10 instructors and can handle up to 100 trainees for each training session.
• The company offers 4 Advanced technology courses, each of which is taught by a team of 4 or more instructors.
• Each instructor is assigned to a maximum of two teaching teams or may be assigned to do research Each trainee undertakes one advanced technology course per training session.

Tables: Instructors, Trainees, Courses, TrainingSessions, InstructorTeams, Assignments, ResearchTasks.

12. Blood Donation System Database Project

A system in which data of patient, data of donor, data of blood bank would be saved and will be interrelation with each other.

Data of Patient – Patient Name, Patient Id, Patient Blood Group, Patent Disease. Data of Donar – Donar Name, Donar Id, Donar Bood Group, Donar Medical report, Donar Address, Donar Contact number. Data of Blood Bank – Blood Bank Name, Blood Bank Address, Blood bank Donor’s name, Blood Bank Contact Number, Blood Bank Address.

Try to implement such scenario in a database, create a schema for it, an ER diagram for it and try to normalize it.

Tables: Patients, Donors, BloodBanks, BloodBankDonors, BloodInventory, PatientDonorMatch.

13. Art Gallery Management Database Project

Design an E-R Diagram for an Art Gallery. Gallery keeps the information about “Artist” their Name, Birthplace, Age & Style of Art about “Art Work,” Artist, the year it was made, Unique title, Type of art & Prices must be stored. The piece of artwork is classified into various kinds like Poetess, Work of the 19th century still life, etc.

Gallery keeps the information about Customers as their Unique name, Address, Total amount of Dollars, they spent on Gallery, and liking of Customers.

Tables: Artists, ArtWorks, ArtTypes, Customers.

14. Hotel Management System Database Project

A hotel is a hive of numerous operations such as front office, booking, and reservation, banquet, finance, HR, inventory, material management, quality management, security, energy management, housekeeping, CRM, and more.  The hotel has some rooms, and these rooms are of different categories. By room category, each room has a different price.

A hotel has some employees to manage the services provided to customers. The customer can book the room either online or by cash payment at the hotel. The customer record is stored in the hotel database which contains customer identity, his address, check-in time, check-out time, etc. hotel provides food and beverages to their customers and generates the bill for this at the time of their check out.

Tables: Rooms, RoomCategories, Employees, Customers, Bookings, Payments, FoodAndBeverages, Bills.

15. School Management System Database Project

Design a database to maintain information about school staff ( staff management system in Ms access ) and students satisfying the following properties:

• Staff will have their id, name, and classes they are teaching.
• The student will be having the name, roll no, section, class.
• Another table containing the section, subject, and teacher information.
• Next will contain fee information for students.
• One contains salary information for teachers.
• Rooms are assigned to classes keeping in mind that there is no time clash of same room or lab, students cannot be entered in more than one section, no student should be there who have not paid fees up to a particular date.

Tables: Staff, Students, SectionSubjectTeacher, StudentFees, TeacherSalaries, RoomAssignments.

16. Wholesale Management System Database Project

• Maintain the details of stock like their id, name, quantity.
• Maintain the details of buyers from which the manager has to buy the stock like buyer id, name, address, stock id to be bought.
• Details of customers i.e. name, address, id.
• Defaulter’s list of customers who have not paid their pending amount.
• List of payments paid or pending.
• The stock that is to buy if quantity goes less than a particular amount.
• Profit calculation for a month.
• Quantity cannot be sold to a customer if the required amount is not present in stock and the date of delivery should be maintained up to which stock can be provided.

Tables: Stocks, Buyers, Customers, Defaulters, Payments, StockReorder, MonthlyProfit, DeliveryDates.

17. Salary Management System Database Project

• Employee list to be maintained having id, name, designation, experience.
• Salary details having employee id, current salary.
• Salary in hand details having employee id, CTC salary, pf deduction or any other deduction and net salary to be given and also maintain details of total savings of employee.
• Salary increments to be given by next year if any depending upon constraints.
• Deduction in monthly salary if any depending upon any discrepancy in work and amount to be deducted.

Tables: Employees, SalaryDetails, SalaryInHand, Savings, SalaryIncrements, MonthlyDeductions.

18. Atm Management System Database Project

Think about yourself and write in the comment. I will pick the best one from the comments and publish it here.

Struggling to find the perfect database project idea?

Don’t worry, I’ve got you covered. I’ve put together a massive list of over 1,000 project ideas just for you.

This list has something for everyone, no matter your skill level. It’s full of diverse and interesting projects that are great for learning, practicing, and even adding something special to your portfolio.

And the best part? You can download the whole list for free.

Just use the coupon code “ FREEDATABASE ” when you’re getting it.

This is my way of helping out and making sure everyone has access to great project ideas. Happy exploring!

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• Knowledge Base

Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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9 Exciting DBMS Project Ideas & Topics For Beginners [2024]

Do you want to work on database projects but don’t know where to start? Then you’ve come to the right place. In today’s article, we’ll discuss some of the most exciting and engaging database project ideas. Check out our  free courses  to get an edge over the competition.

We have discussed all project ideas in detail so you can understand them better and work on them accordingly. Completing projects is a great way to show your knowledge and strengthen your skills. You can choose a project according to your interests and expertise. Let’s get started. 

Data Base Management Systems are a software to store, run queries or retrieve any data. It is very useful because it facilitates storing the data at a centralised location. Also, it reduces redundancy and data inconsistency. The other uses of the Data Management System are-

• Data Indexing
• Query Processing
• Data Independence
• Uniformity in the data administration

Along with its uses, many industries are using DBMS, and it has become the core of their data administration. The industries which use the DBMS are mentioned below-

• Manufacturing
• Social Media
• Telecommunication

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What is DBMS?

DBMS stands for “Database Management System.” It’s a software system designed to manage, store, organize, and retrieve data from a database. A database is a structured collection of data organized and stored to allow for efficient querying, manipulation, and analysis.

A DBMS provides an interface and tools for users and applications to interact with the database without worrying about the underlying complexities of data storage and retrieval. It offers various features and functions that facilitate data management.

Common examples of DBMSs include MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, SQLite, and MongoDB. Each DBMS has its strengths, features, and use cases. The choice of a specific DBMS depends on factors such as the nature of the data, scalability requirements, performance considerations, and application needs.

What are the Types of DBMS?

Several types of Database Management Systems (DBMS) are designed to cater to specific data management needs and scenarios. Here are some of the main types of DBMS:

Relational DBMS (RDBMS)

This is one of the most common types of DBMS. It organizes data into tables with rows (records) and columns (fields) and establishes relationships between tables using keys. SQL is typically used to query and manipulate data. Examples include MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, and SQLite.

Object-Oriented DBMS (OODBMS)

These systems are designed to handle complex data structures, including objects with attributes and methods. They are useful for applications that deal with object-oriented programming languages. Examples include ObjectStore and ObjectDB.

Document DBMS

Also known as document stores or NoSQL databases also store and retrieve data in flexible, schema-less document formats like JSON or XML. They are well-suited for storing and managing semi-structured or unstructured data. Examples include MongoDB, Couchbase, and CouchDB.

Key-Value Stores

These databases store data as key-value pairs, where each value is associated with a unique key. They are efficient for simple read-and-write operations, making them suitable for caching and high-throughput scenarios. Examples include Redis, Amazon DynamoDB, and Riak.

Columnar DBMS

These systems store data in columns rather than rows, which can provide significant performance benefits for certain analytical workloads. They are optimized for data warehousing and business intelligence applications. Examples include Apache Cassandra and Google Big Table.

Graph databases store data in nodes and edges, representing entities and relationships between them. They are designed for managing and querying highly interconnected data, such as social networks or recommendation systems. Examples include Neo4j and Amazon Neptune.

Time Series DBMS

These databases are designed to handle time-stamped data, such as sensor data, logs, and financial market data. They provide efficient storage and querying mechanisms for time-series data patterns. Examples include InfluxDB and OpenTSDB.

NewSQL Databases

These are a new generation of relational databases whose main aim is  to combine the advantages of traditional databases via scalability and performance capabilities. For examples CockroachDB and NuoDB.

Spatial DBMS

These systems, such as geographic information systems (GIS), are specialized for storing and querying spatial data. They enable efficient manipulation and analysis of location-based information. Examples include PostGIS and Oracle Spatial.

In-Memory DBMS

These dbms projects store data completely in memory by offering high-speed data retrieval and processing. They are often used for real-time analytics and applications that require rapid data access. Examples include SAP HANA and VoltDB.

DBMS Project Ideas

The following are some easy and exciting dbms project ideas. Choose one according to your requirements:

1. E-commerce Platform

You must’ve seen multiple online retail platforms. Some great examples of such platforms are Amazon and Flipkart. In this DBMS project, you’ll have to develop a similar e-commerce platform, where a customer can register and buy a product.

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Your developed platform should have a registration window where the user will provide their name, bank name, and bank account number. This information will enable them to make transactions easily. After they’ve registered, the system should give them unique user IDs and customer IDs. They should have the option to set up their passwords. 

Any e-commerce platform would be incomplete without having any products to sell, wouldn’t it? So it should have product listings as well, classified according to their prices (or some other criterion). The user should have the option to buy one or more products from your platform. And after the user makes a purchase, the system would generate an invoice, which will contain the user’s name. The system can allow ordering from different suppliers according to availability. 

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Using DBMS for an e-commerce platform will give you extensive experience in this technology. It’ll help you understand how online platforms use and interact with databases. Still, this project will undoubtedly take some time and effort. 

This project makes for one of the most relatable database projects , as e-commerce uses databases for tracking the transactions, and products database, the marketing team also uses the DBMS to track the traffic, acquire potential customers and retain the existing customers. The e-commerce industry is running on having a strong database because it is online and data is the new oil. Also, just having data is not enough, having the strong ecosystem of having a string core database is what makes it all efficient.

Also try: Full stack project ideas and topics

2. Inventory Management

Every organization has an inventory to manage, which takes up a lot of resources. Usually, an organization would assign the duty of inventory management to two or more people who’ll keep an eye on it and ensure that all the supplies are available. If any item is missing, the manager would order the same. This system works effectively if the organization is tiny, but that’s not always the case.

You can solve this problem of businesses and build an inventory management system. The goals of your design would be the following:

• Increase the inventory turnover
• Optimize the inventory and the stock levels
• Reduce the number of safe stock levels
• Get low material prices.
• Make it easy to understand and access.
• Reduce the operational costs of the inventory (storage cost, insurance cost, etc.)
• Classify the objects in the inventory according to their stock levels

As you can see, an inventory management system will allow its client to save a lot of time and resources. They can find the stock levels in their inventory and plan accordingly. It will make the enterprise more efficient and productive. 

This is one of the beginner-level database projects on this list. You can work on it even if you don’t have much experience with developing database solutions. 

This is one of the good database project ideas , as inventory is the core of any business. Especially after the pandemic hit, most businesses have turned online. Not only the recently turned online businesses but the recent and old businesses were well. If an organisation has a solid core management system for inventory tracking, they could not only increase the business but also can understand the leakages.

The following can be the sub-topics-

• What is inventory management?
• Why inventory management?
• How DBMS is useful for inventory management?
• What are the methods?
• Micro, Medium, and Macro level businesses that use DBMS for inventory.

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3. Railway System

The railway network of our country is one of the most complex public establishments. You can design a database solution for this network and make the management of the same more natural. Your system should have the following pieces of information:

• Station names
• Tracks that connect those stations (to keep things simple, you can assume that only one track runs between two stations)
• Train IDs with names
• Schedules of the trains 

The train schedules should have information on the stations from where the train starts and by when it reaches the destination. It should also include information on which stations it passes through during its journey. 

To keep things simple, you can assume that every train completes its journey within a day, and they run daily. However, you’ll also need to store information on the sequence of the stations a train passes through. For example, if a train starts from Delhi and goes to Kolkata through Lucknow, then you’ll need to add the arrival and departure times of the train for all these stations. Keeping the stations in sequence will allow easy management of trains and their data.

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Till here, the project is rather easy. You can make it more challenging by adding the passenger information of every train such as its coaches, seat numbers, types of coaches, passenger names, and so on. This project might take some time to complete, but it’ll help you showcase your knowledge of database management solutions while solving a significant issue of a public authority. 

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India has a very big network of railways, not only that but the metros also have become a very significant part of commutation over the country. Both these types of railways have a wide network and people’s daily commutes depend upon them. Keeping a track of the train’s arrival, departure, first time of arrival at a particular station and last train departure also have a big role to play as people plan their schedules accordingly. Keeping a track of these is really important. Not only that but also keeping the tracks safe from collisions and any haphazard also is important for the smooth operation of the network. This brings another factor which is revenue, the need for revenue generation arises because of the large number of users. Keeping a track of tickets, and distance travelled is necessary in order to track the overall revenue which eventually helps in the country’s GDP. This is why is one of the important topics for the database project topics .

The sub-topics could include the following-

• What are the types of railway networks?
• How DBMS could be used in the railway industry?
• Advantages of using DBMS in the railway industry.
• Disadvantages of not using the DBMS in the railway industry.

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4. college data management.

Colleges have multiple departments where every department offers many courses. These departments have a head (HOD) and various instructors. Even though there are many instructors, one instructor can only work in one department. As you can see the organization structure of a college is quite complicated and requires a lot of effort to manage. 

In this database project, you can build a solution to tackle this problem. It would store all this information about the college and its departments. However, the information we’ve discussed above isn’t sufficient for a college. We need to mention the courses as well.

A course can have only one instructor, but an instructor can have multiple classes. You’d need to add this information to the database system as well. You can make this project more advanced by adding the course enrollment information. 

You can add the enrollment information of the students as to how many students have taken a particular course. 

The system should allow easy access. Your developed DBMS-based solution would allow a college to save a lot of time and resources; moreover, the user could see all the college information from one place and modify it accordingly. 

Along with that, colleges or universities hold a lot of sensitive information about their students’ such as-

• Personal information
• Bank Details
• Parent’s information, etc.

These kinds of personal information need to be secured from online theft and unauthorised access. The security of this information is the responsibility of the college and universities. And this is why the university management system is one of the important topics for database projects.

The sub-topics could include-

• What is university database management?
• Why is university database management important?
• Advantages and disadvantages of DBMS for university database management.

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5. library data management.

If you’re an avid reader, then chances are, you must’ve gone to a library. And you may already know how many books a library has to keep track of. Libraries don’t have a lot of staff, but they have to keep a record of all the books they have and the books they have lent. You can simplify the management of a library’s data. 

You should start with students and faculties, i.e., people can get books from the library. Now, there would be a significant difference between the number of books a student can get and the number of books a faculty can get. So, add those limits in your system as well. Then, every book would have a unique ID.

Books with the same title and author would have different IDs according to their copies. You’ll have to add entries for every book. And then, add the details of who issued the book and when with the duration of their ownership. Your DBMS-based solution should also have details on the books that people haven’t returned and the due fines. 

Along with keeping the labeling organised for the books, it is equally necessary to modify the data and keep constant track of the books which keeps getting published and returned. Also keeping the track of the worn-out books, restock, newly added books, etc. Along with that, libraries also maintain the data genrewise. All of these constitute the library database management system that helps the libraries run their operations on autopilot and also helps the librarians.

• Types of data libraries stored.
• What is a library database management system?
• Why is library database management important?

Read:  SQL Project Ideas for Beginners

6. Solution for Saving Student Records

You can build a solution that saves student records for an educational institution. Handling student records is no easy feat. You need to keep their name, subjects, fees, any provision of concession, and their academic progress. A DBMS-based solution will allow the client to save a lot of time and effort.

Your design goal should be to have separate files for each student where the data will store information about the student. You can start by adding the following sections:

• Student’s Name
• Subjects (or Stream)
• Grades (or Marks)
• Concessions (or Scholarship)
• Additional Information

It’s one of the easy database project ideas. You can take it a step further, and add the option to include students of different grades or sections. Your designed system should allow the admin to enter the details mentioned above. And the admin should be able to access it easily. 

7. Hospital Data Management

Hospitals have unique data requirements. Not only do they have to maintain the medical records of their patients, but they also have to manage their staff and its multiple departments. You can solve the data-related problems of hospitals by creating a DBMS solution. 

First, you should assign unique IDs to the patients and store the relevant information under the same. You’ll have to add the patient’s name, personal details, contact number, disease name, and the treatment the patient is going through. You’ll also have to mention under which hospital department the patient is (such as cardiac, gastro, etc.).

After that, you should add information about the hospital’s doctors. A doctor can treat multiple patients, and he/she would have a unique ID as well. Doctors would also be classified into different departments. 

Patients would get admitted into rooms, so you’ll need to add that information to your database too. Apart from that, there would be distinct rooms (ICUs and Operation Theaters) in the hospital. Then, you’d have to add the information of ward boys and nurses working in the hospital and assigned to different rooms. 

You can start with a small hospital and expand it as you move on. Make sure that the data is easily understandable and accessible. 

Also, the hospitals have a lot of information with them such as the patient’s history, pharmacy, test results, number of beds, information about the helping staff, etc. All of this data needs to be managed as they are crucial to the hospital’s operations and helps in its smooth functioning. This database management helps in routine or emergency visits as well. 

• Which types of data do the healthcare industry hold?
• What are the advantages of DBMS in the healthcare industry?
• What are the disadvantages of not using DBMS in the healthcare industry?

8. Blood Donation Management

Another DBMS project idea is to create a blood donation clinic. You should start by adding donor names and assigning them unique IDs. Add their details and relevant information such as blood type, medical report, and contact number. Similarly, add patient names with unique IDs, details on their medical conditions, and blood types. 

After you’ve created a database of patients and donors, you can work on a database for the blood bank. There, you’ll have to add the name of the blood bank, its staff details, operating hours, and address. 

DBMS is helpful in the blood donation industry by keeping the track of the acceptors and donations. This helps the hospital in keeping a record of the blood donors as well in case of any emergency. And also to help them keep track of the storage.

9. Payroll Management Solution

Managing payroll is one of the most crucial aspects of an organization. So you can create a database solution for this purpose as well. Here, you’ll first have to assign a unique ID to every employee. And then, you’ll need to add the details of the same employee, such as their name, role, department, etc. You’ll have to add information on the unpaid salary of the employee, and the date on which the salary has to be released. Similarly, you’ll have to add the time from which the salary is due. 

After that, the database would need the information on the HRA, DA, medical allowance, and other additions to the basic pay. Similarly, you’ll need to add data on the deductions you need to make (if there are any) such as extra leaves. You can add more difficulty to the project by creating a management solution for a significant organization. In this one, make sure that you add the departmental details of the employee under his/her ID. 

Also read: Web Developing Project Ideas For Beginners

Read our Popular Articles related to Software Development

Learn more about dbms.

Embarking on a journey into Database Management Systems (DBMS) opens up a world of opportunities for beginners. Understanding the fundamentals of DBMS, including its types such as relational, object-oriented, and distributed systems, lays a solid foundation for diving into practical applications. The provided DBMS project ideas offer a fantastic starting point, enabling enthusiasts to apply theoretical knowledge to real-world scenarios.

Continuously exploring and learning more about DBMS not only enriches one’s understanding of the subject but also equips them with invaluable expertise for future endeavors in the ever-evolving field of data management.

We hope you will have an excellent learning opportunity in executing these projects. If you are interested to learn more about DBMS, Full-stack management and need mentorship from industry experts, check out upGrad & IIIT Banglore’s Executive PG Program Full-Stack Software Development .

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Frequently Asked Questions (FAQs)

Database Management System (DBMS) software applications essentially structure and organize data files to provide easy access and standard data assortment. It essentially categorizes the data system so that the user can derive the required information from heaps of data. While Structured Query Language (SQL) isn’t a database management tool, it’s a programming tool that helps access the database. It is linked to the Relational Database Management System (RDBMS), wherein specific user queries are processed to retrieve desired data from the system.

Database Management Systems (DBMS) have made data storage and retrieval much more accessible. Any DBMS needs to have an excellent design for the data to be stored systematically. The DBMS must be designed so that users can access and navigate through the system quickly. A good design ensures uniformity in the data structure to create a reliable DBMS. It should strategize data to avoid duplication of results and increased storage usage. Data design focuses on creating a simple design structure to prevent complexity in locating data and reduce DBMS maintenance.

A file system is essentially categorized as a Database Management System (DBMS) owing to its purpose. However, compared to DBMS, a file system stores data comparatively primordially. Files are taken and stored categorically; however, like DBMS, their relevancy or connection is not mapped out. Hence, retrieval of files gives a limited search result without deep profiling of the particular file. The file system doesn’t manage repeated data files and update data files from different users either. This creates redundancy in the data system and increases storage usage. The file system categorizes data but doesn’t store it in desirable categories.

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Researchers Develop Groundbreaking Synthetic Database of Tropical Cyclone Events

Unveiling RAFT, a novel model that generated a dataset of 40,000 cyclone events to better assess tropical cyclone-related risks

Tropical cyclones (TCs) cause significant damage to U.S. and Caribbean coastal regions every year. Researchers have developed a database of synthetic TCs to understand TC risk at local-to-regional scales.

(Photo by  12019 | Pixabay )

The Science

Atlantic tropical cyclones (TCs) cause enormous damage. Scientists have sought to understand TC risk at local-to-regional scales. The challenge in understanding that risk lies in the limited historical data and rarity of these storms making landfall, combined with the high cost of simulating storms using advanced climate models. To overcome these hurdles, scientists have crafted a new approach that uses a mix of physics, simple math, and advanced computing (deep neural networks) to create a synthetic record of tropical cyclones. This breakthrough allows them to simulate tens of thousands of synthetic storms with realistic paths, strengths, and rainfall. This innovative model provides a richer, more detailed picture of Atlantic tropical cyclone behavior.

This study pioneers the use of advanced artificial intelligence to create a detailed synthetic record of TCs, offering a groundbreaking tool for enhancing our understanding and preparedness for these natural disasters. This innovative method addresses the critical issue of limited observational data and the prohibitive computational demands of traditional models. Its contributions promise significant advancements in disaster readiness and climate risk analysis, potentially benefiting multiple sectors such as urban development, infrastructure planning, and insurance by refining our capacity to assess and manage the risks posed by TCs.

TCs pose a significant threat to the socio-economic stability of the U.S. and Caribbean coastal regions, making the precise assessment of TC risks at local and regional levels crucial. Conventional methods are limited by the brief historical record of observations and the substantial computational demands of high-resolution climate simulations, leading to challenges in accurately gauging these risks. To bridge this gap, researchers developed a groundbreaking database that includes 40,000 synthetic TCs, crafted using the Risk Analysis Framework for Tropical Cyclones (RAFT) and pioneering the application of advanced artificial intelligence for this purpose. This comprehensive database not only mirrors the historical patterns of TC tracks and intensities with high fidelity but also incorporates data on storm-induced rainfall, thus providing an all-encompassing resource for the analysis of wind and rainfall hazards posed by North Atlantic TCs. Demonstrated by its strong alignment with actual observed data, researchers methodology marks a pivotal advancement in the meticulous evaluation of TC risks, setting the stage for enhanced disaster readiness and more strategic risk management approaches. Further research is ongoing to improve the precipitation simulation and evaluate TC impacts such as urban flooding, power outages, and damage to infrastructure.

PNNL Contact

David Judi,  Pacific Northwest National Laboratory,  [email protected]

L. Ruby Leung, Pacific Northwest National Laboratory,  [email protected]

This work was supported by the Multisector Dynamics and Regional and Global Model Analysis program areas of the Department of Energy, Office of Science, Office of Biological and Environmental Research as part of the multi-program, collaborative Integrated Coastal Modeling project.

Published: March 12, 2024

W. Xu et al. “A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset.” Sci Data 11, 130 (2024) . https://doi.org/10.1038/s41597-024-02952-7

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Development of energy resilience research landscape using bibliometric analysis

• Published: 12 March 2024

Cite this article

• Pidpong Janta 1 ,
• Naraphorn Paoprasert 2 ,
• Pichayaluck Patumwongsakorn 2 ,
• Nuwong Chollacoop 1 &
• Kampanart Silva   ORCID: orcid.org/0000-0003-0818-314X 1  

Energy resilience has recently gained interest in both scientific and policy domains since it helps energy infrastructure withstand environmental impact and contributes to sustainable socio-economic development. Various definitions of energy resilience make it difficult for researchers to identify relevant research topics, research trends, and relationships among topics that suggest areas for collaboration. This study uses bibliometric analysis to develop energy resilience research landscape containing aforementioned information. Search term: energy OR power W/2 resilien* was used to extract bibliometric information from Scopus database. Four Bibliometrix tools, namely Word Growth, Trend Topics, Co-occurrence Network, Co-citation Network were used to develop the research landscape. Most relevant research topics were power grid-related topics, power system resilience against climate change and disasters, and topics often discussed with energy resilience, e.g., reliability and energy efficiency. Trendy research topics were microgrid, power system resilience, reliability, renewable energy, and critical infrastructure. Research topics can be divided into four clusters, namely overarching themes, resilience during disruptions, resilience during normal operations, and technologies contributing to energy resilience, with connections of topics within and between respective clusters. Four distinct definitions of energy resilience were derived from the four clusters in the research landscape and integrated into a comprehensive definition. Research topics identified in the landscape and their relationships synchronize well with global trends and policies. The evidence-based research landscape can facilitate research prioritization, research collaboration, and translation of research into policy and practice.

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This work was supported by funded by the National Science and Technology Development Agency (NSTDA) under the Energy Innovation Program.

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K.S., N.P. and N.C. provided conceptualization, P.J., N.P. and K.S. designed methodology, P.J. and P.P. utilized software, N.P. and K.S. processed validation, P.J., N.P., P.P. and K.S. performed formal analysis, P.J., N.P. and K.S. completed investigation, N.C. and K.S. provided resources, P.J., P.P. and K.S. organized data curation, P.J. and K.S. completed writing-original draft preparation, N.P., N.C. and K.S. performed writing-review and editing, P.J. conducted visualization, N.C. and K.S. processed supervision, K.S. possessed project administration, N.C. and K.S. provided funding acquisition. All authors read and approved the final manuscript.

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Janta, P., Paoprasert, N., Patumwongsakorn, P. et al. Development of energy resilience research landscape using bibliometric analysis. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04745-9

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• 1 Shanghai Maritime University, China

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The floating structure of floating photovoltaic can be attached by aquatic organisms, resulting in changes in the draft depth of the floating body, which can affect the resistance characteristics of the floating body at different water velocities. The analysis for the characteristics of flow field is the key to revealing the change law of resistance under different conditions. The k-ε turbulence model which has been verified by water channel experiment is used to research the influence of draft depths, velocities and number of floating bodies for the drag in the paper. The research results show that the draft depth has more influence on the drag of the single floating body than on the velocity of water flow. The main reason is that the separation of the boundary layer produces a larger separation bubble, which increases the pressure difference between the front and back surfaces of the floating body, leading to a larger entrainment range and reflux velocity in the wake. The high flow velocity will enlarge the influence of the draft depth on the drag. The shielding effect of the tandem floating bodies is reflected in the non-uniform fluctuation of velocity and pressure along the flow direction, which affects the wake development of the tandem floating bodies, resulting in the typical spatial characteristics of resistance at different positions. The increase of the number of tandem floating bodies will further expand the difference of flow field, which can affect the resistance distribution law. The research results can provide theoretical support for the stability design of floating photovoltaic.

Keywords: Floating photovoltaic, Aquatic Organisms, drag, Draft depth, Water flow velocity, Number of floating body

Received: 18 Feb 2024; Accepted: 12 Mar 2024.

Copyright: © 2024 Wang, Xiaolei and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Liu Xiaolei, Shanghai Maritime University, pudong, Shanghai, China

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