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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.

Virtual and Remote Guidance Counselling System Database Design - Relationship

  • 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.

Face Recognition Application Database Design - List of Tables

  • 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.

Invoice Management System Database Design - List of Tables

  • 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.

Loan Management System with SMS Database Design - List of Tables

  • 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.

Exam Management System Database Design - List of Tables

  • 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.

Hospital Resources and Room Utilization Database Design

  • 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.

40 List of DBMS Project Topics and Ideas

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

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-

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DBMS

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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.

E-commerce DBMS

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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.

inventory management

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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.

DBMS in railways

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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.

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?

library database management system

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?

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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Case Studies Examples Scenarios Database System DBMS

Most of the time you see the case studies and scenario-based questions in the Database System (DBMS) paper. Keeping in view, I am sharing with you some of the case study base questions of the database course.

Examples of Case Studies and Scenarios questions from DBMS

  • Examples of Case Studies and scenarios from the Database System.
  • How you can make a database from the scenario mentioned below.
  • How you can normalize the database tables from the case studies mentioned below.
  • How to draw the Entity-relationship diagram from the given case study.
  • How to draw the Data flow diagram from the case studies mentioned below.
  • What database model is suitable for the case studies mentioned below.
  • What kind of database users are suitable for the given case study.
  • What kind of database redundancies and inconsistencies are possible in the given scenario.
  • How You can write SQL Queries on the tables of the mentioned case study.
  • Find the possible database keys from the tables of these case studies.
  • Suggest the relationships among the tables of the given scenarios.
Vehicle information dissemination system for Cloud  Android Project for BCS BSIT MCS BSSE
Gym and Fitness Management System Project IN C# for BCS BSIT MCS BSSE
HR Management System Project in C# and VB.NET for BCS BSIT MCS BSSE
Employees Attendance System via Fingerprint  in C# and VB.NET for BCS BSIT MCS BSSE
Pharmacy Record Management System Project in PHP, ASP or C#.NET
Car information System using Android and Arduino final year Project for BSCS BSIT MCS BSSE
Agile File Master App final year project for BSCS BSIT MCS BSSE
Android Messenger App final year project for BSCS BSIT MCS BSSE
Android Call Recorder App final year project for BSCS BSIT MCS BSSE
Music Listening App final year project for BSCS BSIT MCS BSSE
Like mind matches Android application – Final year project for MCS
Financial Helper Using QR/Barcode Scanner Android Final year project for MCS BSCS BSSE
My Grocery List Mobile Application Project in  Android

If you are still in reading the more case studies, then you can read 100+ case studies .

Related Posts:

  • Case Studies Examples Scenarios OOP
  • History of Database System (DBMS)
  • Data Independence in DBMS (Database)
  • 3 Tier Database Architecture in DBMS
  • Client-server Database Architecture in DBMS
  • Leadership Case Studies MCQs

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  • Databases by Type: Case Studies

Helpful Databases for Finding Case Studies

The following databases contain filters specifically for finding case studies. Keep in mind that in many databases and search tools, adding the phrase "case study" to your search terms can also help you find case studies.

Coverage: 1971-  Full text: Yes

Coverage: 1922- Full Text: Yes Journals and Other Sources Included

  • Computer Science Database This link opens in a new window This collection provides unmatched discipline-specific coverage spanning thousands of publications, many in full text. Subject coverage: Computer Science; Information Systems; Computer Security; Database Design; Software Development; Web Commerce; LANs; WANs; Intranets; Internet. To find case studies, go to the Advanced Search page , go to the box labeled "Document Type," and select "Case Study."
  • Emerald Emerging Markets Case Studies Collection This link opens in a new window Emerald Emerging Markets Case Studies (EEMCS) is an online collection of peer-reviewed case studies focusing on business decision making and management development throughout key global emerging markets . Cases are written by case writers working in or closely with developing economies, offering local perspectives with global appeal.
  • ProQuest One Business This link opens in a new window An intuitive and comprehensive business library containing millions of full-text items across scholarly and popular periodicals, newspapers, market research reports, dissertations, books, videos and more. To find case studies, go to the Advanced Search page , go to the box labeled "Document Type," and select "Case Study."
  • SAGE Journals This link opens in a new window Access more than 650 journals spanning the Humanities, Social Sciences, and Science, Technology, and Medicine. To find case studies, enter your search terms. On the search results page, go to the Article Type filter on the right and select Case Report. Note: you may need to click "More" under Article Type, and you may then need to search for "Case Report" under the Article Type filters.
  • Science Database This link opens in a new window Subject areas include: • Physics • Engineering • Astronomy • Biology • Earth Science • Chemistry • and more. To find case studies, go to the Advanced Search page , go to the box labeled "Document Type," and select "Case Study."
  • SciTech Premium Collection This link opens in a new window Subject areas include: • Advanced technologies • Aerospace • Agricultural science • Aquatic science • Atmospheric science • Biological science • Computer science • Earth science • Environmental science • Engineering • Materials science • Polymer science. To find case studies, go to the Advanced Search page , go to the box labeled "Document Type," and select "Case Study."
  • Technology Collection This link opens in a new window The Technology Collection provides broad indexing coverage of the scholarly literature in advanced technology, computer science, engineering, materials science and related areas. To find case studies, go to the Advanced Search page , go to the box labeled "Document Type," and select "Case Study."
  • Last Updated: Jan 10, 2024 10:39 AM
  • URL: https://libguides.wpi.edu/casestudies

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Case Studies

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  • Sinopay, SaaS Payments Gateway Provider, Expands Globally with MySQL HeatWave on Oracle Cloud Infrastructure
  • datasíntese, leading Brazilian FinTech, boosts performance by 50% and slashes costs by migrating to MySQL HeatWave
  • Bibold Revolutionizes BI Solutions, Boosts Competitiveness with MySQL HeatWave
  • Conecta Wireless delivers unified communications as a service (UCaaS) with MySQL HeatWave
  • Rewards4Earth Migrates its Eco-friendly Rewards App to MySQL HeatWave
  • Procom Achieves 25% Performance Boost and 25% Cost Reduction with MySQL HeatWave on OCI
  • Enpointe IO Achieves 80% Better Performance and 45% Cost Savings with MySQL HeatWave
  • LightSwitch API Fortifies its No-code REST API Developer Platform with MySQL HeatWave
  • Intelectivo, Brazilian ISV, Improves its Web Analytical Platform, Plugger BI by Migrating to MySQL HeatWave
  • tilyanPristka Transforms Business Process Outsourcing Excellence with MySQL HeatWave
  • SKYPlay Cuts Costs 50%, Boosts Gaming Performance with MySQL HeatWave
  • Aiwifi Enriches Analytics Solutions for Wi-Fi Marketing and Customer Experience with MySQL HeatWave In-Memory Machine Learning
  • WelcomeNext Ensures Maximum Availability of E-Learning Solutions with MySQL HeatWave
  • ITSP Boosts App Performance by 100X and Reduces Costs by 33% with MySQL HeatWave
  • Broctagon Fintech Group Migrates its Flagship Forex CRM to MySQL HeatWave
  • Grupo DTG Fuels SaaS Business Growth with MySQL HeatWave after Migrating from Amazon RDS
  • MCM Telecom Boosts Customer Satisfaction to 95% by Moving to MySQL HeatWave
  • UBIT Uses MySQL Heatwave to Build Student Management Systems
  • Aspire Systems Boosts Analytics Performance by 10X with MySQL HeatWave
  • Teyuto, Italian SaaS ISV, Boosts Customer Experiences with Recommendation Engines Built on MySQL HeatWave
  • Exchange Speed Migrated to MySQL HeatWave from Amazon RDS and Redshift to Deliver its High-Performance Trading Platform
  • Gieman, Australian SaaS ISV, Boosts Growth with MySQL HeatWave
  • SaaS ISV Fiscontech Reduces Costs by 95% by Migrating from Amazon Aurora to MySQL HeatWave
  • Fragrantica Enhances User Experience for 25 Million Website Visitors with MySQL HeatWave
  • Dr Mais On-Line, Brazilian TeleMedicine SaaS ISV, increases performance by 50% with MySQL HeatWave
  • Aicoll Improves Loan Default Prediction using Machine Learning Models in MySQL HeatWave
  • Gravity Delivers Scalable Personalization Solutions with MySQL HeatWave
  • Tasmania's Northwest Support Services Modernize the National Disability Insurance Agency with MySQL HeatWave
  • Licitapyme.cl Migrates from AWS to MySQL HeatWave for Improved Performance and Faster Analytics
  • FANCOMI accelerates ad analytics by 10X with MySQL HeatWave
  • Red3i speeds insights by 1,000X with MySQL HeatWave
  • Tetris.co speeds real-time insights with MySQL HeatWave
  • Tamara cuts costs, speeds performance with MySQL HeatWave and Oracle Cloud
  • Wavenet Technology runs one million-plus queries in seconds with MySQL HeatWave
  • Estuda.com increases query responses by 300X with MySQL HeatWave
  • Bionime modernizes data and analytics with Oracle MySQL HeatWave on AWS
  • Centroid simplifies and scales data and analytics with MySQL HeatWave on AWS
  • Johnny Bytes boosts data and analytics with Oracle MySQL HeatWave on AWS
  • KBG Services Payroll Application Increases Data Security and Compliance with MySQL HeatWave
  • Wavenet Technology Saves 30% by Migrating from Amazon Redshift of MySQL HeatWave
  • Shanrohi Cuts Server Costs by 50%, Boosts Performance by Moving from AWS to MySQL HeatWave on OCI
  • Toffs Technologies Boosts Efficiencies by 50% with MySQL Database Service on Oracle Cloud
  • Baguio Relaunches Local Tourism Industry and Protects Visitors with MySQL HeatWave on Oracle Cloud
  • Science House Medicals Speeds Medical Testing by 4x with MySQL HeatWave
  • Uangel Launches New Global Mobile Service with MySQL Database Service on Oracle Cloud
  • College of Marshall Islands Improves Application Performance with MySQL Database Service on Oracle Cloud
  • Akna Lifts Performance by 1000x and Cuts Costs by 60%
  • POCT Science House Quadruples Diagnosis Speed with MySQL HeatWave
  • Asahimatsu Foods builds a low-cost Supply and Demand application in the cloud using MySQL HeatWave Database Service
  • Genius Sonority speeds game analytics by 90X with MySQL HeatWave
  • Noorisys Technologies migrates from AWS EC2 to MySQL HeatWave
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  • VizSeek: AI-based Visual Search Platform Deployment on MySQL and Oracle Cloud
  • Ecopaynet Shortens Time to Market with MySQL Database Service
  • Custella Boosts its SaaS Application with MySQL Database Service
  • AK Systems Drives Innovation with Life Sciences SaaS Application
  • Pasona Tech Reduced Costs by 75% After Migrating from Amazon RDS
  • QBS System Speeds Performance and Tightens Security with MySQL Database Service on Oracle Cloud
  • IsoEnergy Streamlines Million Dollar Drill Programs with MySQL Database Service
  • The Gold Continent Helps Zambia Transition to a Vibrant Formal Economy with MySQL Database Service
  • Sectona Boosts Cybersecurity Offering with Embedded MySQL Database
  • RTTS’ QuerySurge Automates Data Testing with MySQL Embedded as Powerful Backend
  • appleple uses geometry type of MySQL to create a website using location information
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MySQL Enterprise Edition

  • Pinkbyte Inc. and its Subsidiary Mazzzing Inc. Deliver Low-cost, Secure Desktop by Migrating to MySQL Enterprise Edition from Microsoft SQL Server
  • NAVER, Korea’s largest search engine, powers online services with MySQL Enterprise Edition
  • Dream D&S Provides High Security by Using MySQL Enterprise Edition for its Embedded Products, Enabling Customers to Innovate Faster
  • OP-CBS Secures Data with MySQL Enterprise Edition - Unlocking New Business Opportunities
  • Brown University Enhances Campus Database Services with MySQL Enterprise Edition
  • Universidad Complutense de Madrid Maximizes Availability with MySQL Enterprise Edition
  • DGB Capital Powers its Financial Services with MySQL Enterprise Edition
  • Great HealthWorks Improves Reliability by Migrating from MariaDB
  • guard.me Relies on MySQL Enterprise Edition for Enhanced Security and Compliance
  • Toss Bank Delivers Innovative Financial Services with MySQL Enterprise Edition
  • Digital14 Relies on MySQL Enterprise Edition for Enhanced Security
  • ST Engineering's Smart Mobility Rail Business uses MySQL Enterprise Edition
  • UL Solutions Sdn Bhd Delivers its Shop Floor and Inventory Control Application with MySQL Enterprise Edition
  • GCI achieves carrier-grade uptime and slashes IT costs with MySQL Enterprise Edition
  • KAI Improves Railway Efficiencies with IoT Platform using MySQL Enterprise Edition
  • The BBC Ensures World Class Broadcasting Services using MySQL Enterprise Edition
  • Korea Investment & Securities boosts employee productivity with MySQL
  • Meritz Fire Powers Groupware Portal for Improved Collaboration and Cuts TCO with MySQL
  • Itaú Unibanco Boosts Digital Platform with MySQL Enterprise Edition for High Availability and Support
  • SSG Builds Online Shopping Mall using MySQL Enterprise Edition
  • KDDI prevents service downtime with MySQL InnoDB Cluster and reduces failure recovery time by 80%
  • BSE Takes Online Trading from Milliseconds to Microseconds with MySQL Enterprise Edition
  • TMON Builds ‪Korea’s Number One Online Malling Platform ‬‬‬on MySQL Enterprise Edition‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
  • Flash Networks Embeds MySQL Enterprise Edition in Parental Control Solution for Mobile Operators
  • Globo Adopts MySQL Enterprise Edition as Platform for Content Development
  • University of Toronto Empowers Astronomers to Research Dark Matter with Massive Space Image Database
  • SQUARE ENIX reduced database backup time with MySQL Enterprise Edition to 1/6th to offer superb game environment for users worldwide
  • K Bank Delivers High-Quality Customer Services While Reducing TCO by 80% MySQL Enterprise Edition
  • France Billet Stakes Online TicketSales on High Availability of MySQL Enteperprise Edition
  • Mobitel Achieves Optimum Price Performance Ratio for 2 Tier Applications
  • Plusnet Supports Rapid Growth of Customer Base by Improving Visibility, Performance, and Scalability
  • NJ India Invest Boosts Financial Transaction Processing with MySQL Enterprise Edition
  • Isibet Achieve 24x7 Uptime with High Availability MySQL Enterprise
  • LINE Enhances Database Availability, Scalability, and Security with MySQL
  • Credorax Delivers NextGen Payment Processing Technology using MySQL
  • WealthObjects Relies on MySQL to Deliver Innovative FinTech Solutions

MySQL NDB Cluster

  • Beezz Helps Solve IoT Security Threats with MySQL Cluster Carrier Grade Edition
  • Certigna Gains Maximum Availability Through Enhance Performance of MySQL Cluster Carrier Grade Edition
  • BitCash Supports Future Business Growth with MySQL Cluster Carrier Grade Edition
  • DMM.com levaraged Oracle Premier Support for MySQL to upgrade over 100 MySQL Servers in a year
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Enrich your students’ educational experience with case-based teaching

The NCCSTS Case Collection, created and curated by the National Center for Case Study Teaching in Science, on behalf of the University at Buffalo, contains nearly a thousand peer-reviewed case studies on a variety of topics in all areas of science.

Cases (only) are freely accessible; subscription is required for access to teaching notes and answer keys.

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Development of the NCCSTS Case Collection was originally funded by major grants to the University at Buffalo from the National Science Foundation , The Pew Charitable Trusts , and the U.S. Department of Education .

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Where Can I find Harvard Business School Case Studies?

How do i find articles with case studies, where can i find free case studies, subject specialists.

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Harvard Business Publishing makes a great deal of money selling these for business school course packs and will not make them available to libraries. You can, however, order them directly from HBS, around $8.95 each How to find them:

  • Harvard Business Review publishes one case study per issue. These generally deal with fictitious companies but are very good studies of current problems faced by companies.
  • Harvard Business School Publishing Search by company name or topic. Abstracts are usually included. Harvard also sells cases from Babson College and Northwestern's Kellogg School of Management, among others.

Use keyword searches in article databases . For example: "case studies and airlines" or "case  studies and management". Full-text articles and abstracts are available, depending on the journal.

Tip: Use the subject heading "case studies" in ABI/INFORM and Business Source Complete

Article database that indexes academic journals, trade publications, newspapers and magazines in business and economics. Full text is often available. Use the FindIt links to locate full text of articles that are not included in the database.

  • Business Source Complete This link opens in a new window & more less... Article database that includes trade publications, academic journals, industry profiles, country information and company profiles, which include SWOT analyses. Full text is often available. Use the FindIt links to locate full text of articles that are not included in the database.
  • EconLit with Full Text This link opens in a new window & more less... EconLit indexes articles from economics journals, books, book chapters, dissertations and working papers. It is a very good source for empirical studies on economics and finance. Use the FindIt links to locate full text of articles that are not included in the database.

Most cases published for teaching in business schools are not free to use. These are a few resources that do offer free cases, but only LearningEdge offers their entire catalog for free.

  • LearningEdge Cases developed at the MIT Sloan School of Management.
  • Free cases from Stanford Graduate School of Business More are available for purchase through Harvard Business School Publishing
  • Free cases from the Case Centre A selection of cases. Many more available for purchase
  • Subjects: Business
  • Tags: harvard
  • Updated: Sep 6, 2023 3:16 PM
  • URL: https://guides.lib.uchicago.edu/case_studies
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Case Studies: Databases

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  • HBR Case Studies
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Library Databases

Find case studies using the library databases   on many topics, such as business, psychology, public administration, etc., it's easy when you use journal finder..

Option 1: Search by a journal title for case studies.

Start at the Business Library homepage . Look for the SEARCH E-JOURNALS search box.

Type in the SEARCH E-JOURNALS search box and enter the title of the journal. Select the database from the dropdown. Click Search within this publication .

Consider these journals:

  • Journal of Information Technology Teaching Cases
  • International Journal of Management Cases : IJMC
  • Journal of the International Academy for Case Studies
  • Business Case Journal
  • Journal of Critical Incidents

Option 2: Find journal titles and then search for case studies.

In the SEARCH E-JOURNALS search box type case studies or case research . Browse the journal title list and select a title - click the database (under the Journal's name). Click Search within this publication.

case study topics database

Click Search within this publication . The database opens.

Now, click search and browse the full list of available case studies. Filter by publication year.

Add a simple keyword to focus your search on your area of interest. Keep the concept broad: management, strategic, discrimination, information management.

case study topics database

Search tips for the library catalog & databases:

  • Keyword search: Add quotation marks to group significant words together. For example: "case studies" "supply chain".
  • Filter your results: If the database has the menu option, select case studies. For example, Business Source Complete. Advanced Search screen - Publication Type - Case Studies.
  • The term "case study" often appears in the document title- try adding "case study" to a title search
  • Try searching with various terms, case study or case studies ; or use punctuation to find all forms - type this: case stud*

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Case Study: How a bank turned challenges into opportunities to serve its customers using NoSQL Database

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Acknowledgements: Michael Brey, Director of NoSQL Database Development, Oracle 

An industry in flux

Financial services industries are at crossroads and are experiencing massive changes in response to shifting customer demands. With the increasing adoption of cloud technologies, digital-only enterprises are offering innovative solutions at the lowest cost.

Customer experience is a strategic imperative for most organizations today, but delivering an engaging experience across the growing number of digital customer touchpoints can be challenging, especially if they have an aging technology stack.

Additionally, organizations have to navigate these transformational changes while managing vast volumes of digital transactions, a variety of data, and velocity without straining their business systems, experiencing data loss, breaches, and/or downtime.

The below graphic shows the IT priorities of financial services institutions, and it is no surprise that 25% of them want to modernize their systems and equally the same % want to ramp up on their digital touchpoints.

case study topics database

This blog will examine how one of India's leading private banks modernized and expedited its digital presence, providing an enhanced experience for their customers using Oracle NoSQL Database . 

Some of the bank's challenge:

  • Exceeding customer expectations : India  has more than 50% of its population below age 25 and more than 65% below age 35 . Banks customers are increasingly comparing banking experiences to other areas of their digital lives. These digital natives aren't looking to check their balances and deposit checks. They are looking for more meaningful online experiences, e.g., they are looking to start and finish applications to open an account without ever walking into a bank, and they want it to happen fast. The bank was looking at a system that can provide an engaging and personalized digital customer experience in real-time under strict SLA (e.g., process a loan under 10 sec).
  • Ability to provide comprehensive services : Provide 'Always-on' digital services and delight customers by assisting them through chatbot interactions.  Additionally, they want to experiment and deliver new services such as enhanced payment and block-chain technologies valued by their customers.
  • Provide customer 360 experience : The bank offers various services, and their customers interact with those services in many different ways. However, customers want a consistent experience, regardless of the business division they are interacting with or the device they use in the process. Delivering an engaging and personalized customer experience with a single customer view and a unified view of all interactions encompassing each touchpoint with the bank is challenging.
  • Managing change without disruption : The bank needed agility to launch new services and make their development staff more productive. They want to minimize outages with high availability built into the system.

Choosing the right data management strategy

A comprehensive data management strategy sets the stage for establishing a deeper understanding of customer experience. It can offer a single view by collecting all the customer's structured and unstructured data from across the organization and other relevant external sources into one place. A NoSQL database is an ideal choice. It can store personal and demographic information and customer interactions with the company, including calls, chats, emails, texts, social media responses, product/service activity history, past and present purchases. McKinsey's study suggests that data-driven companies tend to be 19X profitable when they use data as a differentiation, as they tend to acquire 23X more customers and retain 6X more customers.

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Source: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance

Why Oracle NoSQL Database

Oracle NoSQL Database multi-data model makes it easy for developers to store and combine data of any structure within the database without giving up sophisticated validation rules to govern data quality.

  • Support for flexible data model:

With the JSON document model, the schema can be dynamically modified without application or database downtime. Bank can localize all data for a given entity – such as a financial asset class or user class – into a single document, rather than spreading it across multiple relational tables. Customers can access entire documents in a single database operation, rather than joining separate tables spread across the database. As a result of this data localization, application performance is often much higher when using Oracle NoSQL Database, which can be the decisive factor in improving customer experience.

  • Predictable scalability with always-on availability

As banking use cases evolve, data sources and attributes grow. Onboarding additional applications, various digital channels, and users' demands, processing and storage capacity quickly grow.

Oracle NoSQL database supports scale-out architecture and sharding technology. With sharding, the data is distributed across multiple database instances spread across different machines, thus overcoming limitations of a single server and associated resources such as CPU, RAM, or I/O. An Oracle NoSQL cluster can be expanded horizontally online without incurring any application downtime and one hundred percent transparent to the application. Oracle NoSQL Database maintains multiple copies of data for high availability purposes.

  • Scale-out architecture for business continuity

The bank needed the ability to deploy the system across multiple data centers for disaster recovery purposes and also for the ability to perform local writes to the data center. Oracle NoSQL Database supports active-active architecture with multi-region tables. A multi-region architecture is two or more independent, geographically distributed Oracle NoSQL Database clusters bridged by bi-directional replication, ensuring the customers always have fast access to services and the latest data.

  • Simplify application development with rich query and APIs

Oracle NoSQL provides a rich query language and extensive secondary indexes giving users fast and flexible access to data with any query pattern. This can range from simple key-value lookups to complex search, traversals, and aggregations across rich data structures, including embedded sub-documents and arrays.  It also supports several easy-to-use SDKs in various programming languages – in particular, the customer was looking at NodeJS drivers.

High-level architecture of the proposed solution

case study topics database

Critical components in the architecture include:

  • Applications Layer:  This layer manages all user input applications, e.g., loan or credit card applications. The applications are based on forms technology, allowing the developers to create adaptive and responsive documents to capture information. The forms have a notion of fragments that allows for pulling out standard segments such as personal details like name and address, family details, income details, etc. The application layer is responsible for doing all the "application plumbing": interacting with the database, enforcing validation at event points, etc. It interacts with the bank's backend system through the API gateway and doesn't store any personal or sensitive information.
  • Database Layer:  A CRM system is used primarily for lead generation to target customers. Also available in this layer is the ELK stack (Elasticsearch, Logstash, Kibana), which is primarily used to audit the log data stored in the NoSQL Database. Oracle NoSQL Database has an out-of-box integration with Elasticsearch. Oracle NoSQL Database also feeds the user drop-off (incomplete form activity) data to the orchestration framework primarily used for retargeting the users.
  • Marketing Layer : This layer hosts various servers that drive the business decision process. It comprises servers and tools used for customer segmentation (identify groups of individuals who are similar in attitudes, demographic profile, etc.) and customer journey analysis (a sum of all customer experiences with the bank).  Additionally, it handles personalization (showing the product or service a customer would be interested in buying) and retargeting (persuading the potential customers to reconsider bank's products and services after they left or got dropped off from their app) based on the drop-off campaign's data that's coming out the Oracle NoSQL Database.

Banking experience re-imagined

A typical user's journey, e.g., loan processing, starts with a user interacting with banks loan processing applications via – the web, mobile device, email, or even branch. The application is served off the forms in the application layer. At this stage, the user fills in details and submits the scanned supporting documents. These scanned forms are classified, and information is extracted, and the data is sent to the NoSQL Database store. The data is sent to the processing system that triggers the underwriting process, beginning with the rule engine and credit scoring engine. Depending on the underwriting process results, an application will be approved, denied, or sent back to the user for additional information. If the application is approved, the loan amount is deposited into the user's account. Suppose the user drops off at any point while filling the form. In that case, this drop-off information is stored in the NoSQL Database and feeds into the orchestration system to kick start the retargeting campaign that allows the bank to target the customer who got dropped off.  The process is repeated with specific ads, emails, or WhatsApp messages retargeting the customers. In the event the customer returns, they can start the journey where they left off.

In conclusion, one of India's leading private banks modernized and expedited its digital presence and provided an enhanced experience for its customers using Oracle NoSQL Database. 

More information

Oracle NoSQL Database is a multi-model, multi-region database designed to provide a highly-available, scalable, flexible, high-performant, and reliable data management solution to meet today's most demanding workloads. It is well-suited for high volume and velocity workloads, like the Internet of Things, customer 360, online contextual advertising, fraud detection, mobile application, user personalization, and online gaming. Developers can use a single application interface to build applications that run in on-premise and cloud environments quickly.  Visit NoSQL Database Cloud Service page  to learn more.

Michael Brey

Director of nosql development.

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Data Science Case Studies

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Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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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

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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 .

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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 .

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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 .

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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 .

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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.

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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 .

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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.

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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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Graph Database Use Cases

One of the primary advantages of using a graph database is the ability to present the relationships that exist between datasets and files. Much of the data is connected, and graph database use cases are increasingly helping to find and explore these relationships and develop new conclusions. Additionally, graph databases are designed for quick data […]

graph database use cases

One of the primary advantages of using a graph database is the ability to present the relationships that exist between datasets and files. Much of the data is connected, and graph database use cases are increasingly helping to find and explore these relationships and develop new conclusions. Additionally, graph databases are designed for quick data retrieval. 

case study topics database

Algorithms can be used when analyzing graphs. They can explore the paths and distances between vertices, the clustering of vertices, and the relevance of the vertices. The algorithms often examine incoming edges and the importance of neighboring vertices. 

Applying  algorithms  to graphs allows researchers to apply pattern recognition, machine learning, and statistical analysis. When massive amounts of data are processed, this process provides a more efficient analysis.

In a DATAVERSITY®  interview , Gaurav Deshpande, vice president of marketing for TigerGraph, said,

“Whenever customers ask me about graph databases, I keep it very simple. When you hear the word ‘graph,’ graph is equal to ‘relationship.’ So, any time you are trying to do analysis of relationships, that’s where you should use the graph database. And given that all of us are increasingly more connected to each other – both as people and as organizations, as entities – it just makes sense that graph databases would become more prominent and more important as time goes by.”

Graph databases are designed to store relationships, so algorithms and queries can be used to perform their tasks in subseconds rather than minutes or hours. Users aren’t required to perform countless joins, and  machine learning  and  data analytics  operate more efficiently. While not known for being user-friendly, graph databases tend to operate more efficiently than  SQL systems .

The Two Types of Data Graphs

There are two basic types of data graphs:  property graphs  and  RDF graphs . The property graph focuses on data integration, while the RDF graph deals with analytics and querying. Both forms of graph are made up of points (vertices) and their connections between the points (edges). However, there are several differences.

Property graphs focus on data integration and are used to model relationships between the data. They support query and data analytics based on these relationships. A property graph’s vertices can contain detailed information on a subject, while the edges express relationships between the vertices.

The resource description framework (RDF) model is designed to represent statements. A statement contains three elements – two vertices that are connected by an edge. Each vertex and edge has a unique resource identifier (URI) that is used for identifying and locating it. The RDF model offers a way to publish the data using a standardized format with well-defined semantics. Pharmaceutical businesses, health care companies, and government agencies working with statistics are examples of organizations that have begun using RDF graphs.

RDF graphs are especially useful for showing  master data  (aka essential data – names, addresses, phone numbers that provide context for transactions) and complex metadata. RDF graphs are commonly used to express complex ideas in a domain, or when circumstances require rich semantics.

Because SQL databases and graph databases have significantly different designs, each comes with its own strengths and weaknesses. Graph databases can be used to resolve a variety of problems. Below are just a few popular graph database use cases.

Detecting Bank Fraud:  One form of bank fraud is called “mule fraud,” and involves a person who is called the “money mule.” This person transfers or deposits money into their own account, and then the money is transferred to a partner in the scam, who is often in another country. 

Traditional SQL systems will create alerts regarding suspicious accounts, which are then flagged by a human. Unfortunately, because of the limited information SQL systems communicate about these accounts, questionable behavior can go unrecognized.

Often these accounts will share similar information (addresses and telephone numbers) that is required for opening the accounts. While criminals may use two or three names, they typically use one phone number and one mailing address. With graph-based queries, bank security can quickly identify accounts with the same phone numbers, addresses, or similar connections, and flag them for further investigation.

This method can use machine learning models that have been trained to identify money mules and their fraud behaviors.

Customer Marketing:  A key aspect of marketing is determining what the customer wants. In a  data-driven business environment, marketers study the relationships customers have with each other and with various products, as well as the relationships that exist between different products. (An individual purchases a pregnancy test, and from the same store the next day purchases three books on how to have a healthy baby). This helps marketers determine what the customers want. Marketers attempt to offer the customers what they want before they have purchased it, with the goal of making a profit.  

Today, many companies have collected the following information about their customers.

  • Master data:  age, name, gender, and address
  • Customer research:  web click streams, traffic lines, call logs, etc.
  • Transaction history:  purchases, purchase time, types of purchases
  • Customer predictions:  purchase histories, search histories, cart abandonment, and social media profiles

While many businesses collect this information, they often are unable to use it comprehensively, because the data is not interconnected. However, this data can be integrated using graph technology, allowing researchers to view all the information surrounding a customer. 

With the use of graphs, marketers can develop a better understanding of their customers and the customers’ relationships with each other and with various products.

After identifying relationships the customers have with each other, and with purchased products, the graph researchers can run algorithms that provide more finely tuned predictions about the customer.

Data Lineage:  As data continues to grow in volume, managing it while ensuring data privacy and compliance with laws and regulations has become increasingly difficult. Data can be extremely difficult to track, and locating the source of unwanted changes can also be difficult. Discovering what data is stored in each database as it is moved around and transformed can be extremely problematic.

Graph databases are excellent for tracking  data lineage . The data’s life cycle moves through a variety of steps, and graph databases can follow it, vertex by vertex, by tracking the edges. With graphs, it is possible to see how the information was used, where it was copied, and its original source. 

Manufacturing Traceability:  Manufacturers find traceability to be a very useful process. For example, a flashlight manufacturer might need to issue a recall on a flashlight model because it has a defective component that was purchased from multiple sources. But locating the source of the problem and the specific flashlights affected can be a challenge.

Many manufacturing companies use a production database that manages the product’s lot information, but they also have a retail database, a purchase database, and a shipping database. This complicated situation makes discovering all the relevant information hard to find and organize. 

A graph database is ideal for connecting all the relationships, and graph algorithms can be used to highlight the connections and relevant information.

Criminal Investigations:  Graph databases have recently been used to revolutionize criminal activity analysis. This is generally not used for small, opportunistic crimes, but for crimes involving many interconnected people, businesses, gangs, and locations. 

Graphs can provide an efficient way of identifying criminals and their networks. Graph-based algorithms (such as  PageRank , which uses a centrality process) can be used to discover insights regarding locations, look for important people, and identify potential criminal gangs. Researchers can find the “weakest link” in the graph, meaning the vertex that the graph is based on. If that vertex is removed, the graph, as a whole, may fall apart. This does not mean there’s a problem, but that the linchpin of a criminal organization has been found.

The Graph Database Mission

The  mission  of graph databases and graph database use cases is to provide an understanding of the relationships that exist between data elements, offering analytics that can identify business opportunities and support a foundation for AI/ML projects. It is one of the most significant innovations to evolve from NoSQL databases, storing the relationships between data objects inside the objects themselves, in turn supporting analytics that are almost impossible to produce by other databases.

Ideally, graph databases will work alongside a SQL database – which is still the data workhorse of choice for most organizations.

Image used under license from Shutterstock.com

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COMP 317: Case Studies in Professional and Technical Writing

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  • Cite Your Sources

What is a Database?

Databases are compilations of information in an organized and searchable system. 

An example of a database you're probably quite familiar with is Netflix. Netflix is a database of movies. It's a bunch of movies organized in a way that's very logical. It's organized and broken down by subject areas such as Comedies and Action films. These subject areas are a way of helping you search for information. You can also type in keywords into Netflix such as a title, actor's name, or director's name in Netflix and that information will pop up in the form film results. Like Netflix, we have databases full of specific kinds of resources for you to find information in. In this class, you're specifically looking for case studies. Below you'll find links to a variety of databases where you can kind case studies in different subject areas. 

This video gives examples of how to find case studies in four databases, APA PsycInfo , ASM Failure Analysis Center , Business Source Complete , and Sage Research Methods . More databases to find case studies in are listed below.

Databases with Case Studies

  • ABI/INFORM Complete ABI/INFORM Collection provides coverage of companies and business trends around the world. To find case studies: 1. Go to the Advanced Search page 2. Enter your keywords 3. Go to the box labeled "Document Type," and select "Case Study."
  • APA PsycInfo Covers the professional and academic literature in psychology and related disciplines including medicine, psychiatry, nursing, sociology, education, pharmacology, physiology, linguistics, and other areas. Includes more than 3.7 million records. 1. Search using your keywords 2. Click search 3. Filter your results to "clinical case studies" under "Methodology" in the left side menu
  • Business Insights Global Includes case studies from a wide variety of journals and institutions. After opening the database 1. Navigate to "advanced search" 2. Enter your keywords into the search box 3. Then scroll down to "search limiters" and select "case studies" under "content type"
  • Business Source Complete Business Source Complete contains over 2,000 active, business-related periodicals covering topics such as management, economics, finance, accounting, marketing, banking, and international business. This includes full text of management journals such as Harvard Business Review*, Academy of Management Journal, and the Journal of Marketing. 1. Click on advanced for search screen 2. Type your keywords or subject 3. Look to the bottom left of screen, under "publication type" select "case study"
  • Computer Science This collection provides unmatched discipline-specific coverage spanning thousands of publications, many in full text. Subject coverage: Computer Science; Information Systems; Computer Security; Database Design; Software Development; Web Commerce; LANs; WANs; Intranets; Internet. To find case studies: 1. Go to the Advanced Search page 2. Enter keywords 3. Go to the box labeled "Document Type," and select "Case Study."
  • Engineering Case Studies Online Database of key engineering failures and case studies. Browse by case
  • Sage Research Methods Provides an overview of research methods (quantitative, qualitative and mixed methods) across the social and behavioral sciences. Includes SAGE book and reference material on research methods, and editorially selected material from SAGE journals. Also includes a searchable collection of case studies of real social research, commissioned and designed to help users understand abstract methodological concepts in practice. To browse case studies: 1. Select "Cases" 2. Select a subject area you are interested in (ie. Anthropology, Education, History, etc) 3. Browse cases To search: 1. From the main landing page, enter your keywords and click search. 2. in the left hand menu, select "cases" under content type
  • ASM Failure Analysis Center The ASM Failure Analysis Center features over 1,000 case histories along with authoritative handbook information on failure mechanisms and analysis methods. Relevant mostly for engineering and materials sciences.
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Why Adopting GenAI Is So Difficult

  • Terence Tse,
  • Mark Esposito,

case study topics database

It may seem like everyone’s figured out how to use this new technology. They haven’t.

More than a year after the launch of ChatGPT, companies are still facing the same question when they first considered the technology: How do they actually go about putting it into business use? Many companies have simply discovered that generative AI tools like LLMs, while impressive, aren’t plug and play. Companies should consider a few suggestions when thinking of whether and how to onboard these tools: 1) choose performance over novelty, 2) combine GenAI with tools like vector databases, 3) never forget the human-in-the-loop, 4) trace your data, and 5) have realistic expectations.

In the nearly year and a half since the release of ChatGPT 3.5, both businesses and individuals alike rushed to explore generative AI (GenAI) technologies. For many, there was a palpable fear of missing out on the next big thing, of being overtaken by competitors who were able to crack the code of using it to revolutionize their businesses, or being caught flat-footed by sweeping, industry-wide change. Report after report has touted the transformative power of GenAI across industries and its implications on the future of work. Adding more heat to the fire, media articles continuously reminded us that jobs would likely be lost at scale and speedily .

case study topics database

  • TT Terence Tse is Professor of Finance at Hult International Business School as well as Co-Founder and Executive Director of Nexus FrontierTech. In addition, he co-founded of The Chart Thinktank.
  • Mark Esposito is Professor at Hult International Business School and Faculty Affiliate at Harvard’s Center for International Development at Kennedy School. He co-founded Nexus FrontierTech and The Chart Thinktank.
  • DG Danny Goh is Co-Founder and Chief Executive Officer at Nexus FrontierTech and Entrepreneurship Expert at the Said Business School, University of Oxford. In addition, he co-founded The Chart Thinktank and AI Native Foundation.
  • PL Paul Lee is the Co-Founder of Ximplar, Vocofy AI, AUMEO, and The Chart Thinktank.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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A worrying number of ski resorts could be snow-free in decades, study says

case study topics database

Ski resorts will see significantly less snow cover this century as the globe warms, a new study released this week suggests. In fact, according to the study, natural snow cover could disappear completely by 2100 at 13% of ski resorts worldwide.

"Climate change is significantly altering the patterns of natural snowfall, which has strong but different consequences for ski resorts worldwide," said study lead author Veronika Mitterwallner , a researcher at the University of Bayreuth in Germany.

Worryingly, the authors also predict "the economic profitability of ski resorts will fall globally" as the planet continues to warm.

The study looked at seven mountain ranges around the globe, including the Rockies and the Appalachians. "In all major ski regions, a substantial decrease in the number of days with natural snow cover is expected," said Mitterwallner.

What's the worse-case scenario?

In the "worst-case" future global warming scenario, one-in-eight ski areas are predicted to lose all natural snow cover by 2071-2100, relative to their historic baselines. Twenty percent will lose more than half of their snow cover days per year.

By 2071–2100, average annual snow cover days were predicted to decline most in the Australian Alps (78%) and Southern Alps (51%), followed by the Japanese Alps (50%), the Andes (43%), the European Alps (42%), and Appalachians (37%), with the Rocky Mountains predicted to experience the least decline at 23% relative to historic baselines.

More: Climate change terrifies the ski industry. Here's what could happen in a warming world.

Could ski resorts adapt?

The researchers said that the decreasing snow cover will drive ski tourism to expand, particularly at higher altitudes and therefore in less-populated areas. However, this would pose a threat to alpine plants and animals that are already under climate-related stress.

"Our results indicate potentially negative developments both for the recreational and economic value of skiing and for mountain biodiversity, as endangered high mountain species could be threatened by the loss of space due to the expansion of ski resorts," said Mitterwallner.

Expert says study has flaws

Daniel Scott of the University of Waterloo , an expert on skiing and climate change, reviewed the study for USA TODAY and critiqued the findings.

Scott noted the study didn't account for the depth of snow. "Any snow can mean 1 inch or 10 feet. You can’t ski on 1 inch. The bottom line is that ‘snow cover’ is not a measurement relevant to ski operations or the ski industry," Scott said.

The study also did not factor in snowmaking, he said. "This also does not represent current operating realities."

The study was published on March 13, 2024, in the journal PLOS-One .

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