Principles Of Database Management

The Practical Guide to Storing, Managing and Analyzing Big and Small Data

To be published by Cambridge University Press

This book covers the principles of database management. It starts by defining databases and the various steps of database design. A next part zooms into different types of database systems: pre-relational, relational, object oriented, XML and no-SQL databases. Subsequent chapters discuss transaction management and physical data storage aspects. Also data access and data integration in an x-tier environment are extensively covered. The book concludes by discussing data warehousing, big data and analytics. Throughout the book, we will include various examples and case studies to illustrate and clarify the concepts discussed. Every chapter will conclude with a set of self-study questions such that the book can be easily used as a textbook by colleague instructors. We will also extensively report on both our research and industry experience on the topic to further illustrate the practical impact of the concepts discussed.

The book is the result of having taught an undergraduate database management class and a postgraduate advanced database management class for more than 10 years now. The undergraduate class was attended by students from a variety of different backgrounds (on average 120 students per year). Throughout these years, we have found no good textbook which covers the material in a comprehensive way without getting flooded by theoretical detail and losing focus. Hence, after having teamed up together, we decided to start writing a book ourselves. We believe the outstanding features of our book originate from the strength of our author team.

Given the above considerations, the key distinctive features of our book are:

• The right balance between theory and practice
• End-to-end coverage starting with legacy technologies to emerging trends such as Big Data, NoSQL databases, data quality, etc.
• A unique perspective on how lessons learnt from past data management could be relevant in today’s technology setting (e.g. navigational access and its perils in Codasyl and XML/OO databases)
• A critical reflection and accompanying risk management considerations when implementing the various technologies considered
• The inclusion of exercises and case studies originating from a diversified and complimentary business experience

Target audience

The target audience of our book consists of:

• Under- or postgraduate students taking courses on database management in BSc and MSc programmes on Information Management and/or Computer Science
• Business professionals who would like to refresh or update their knowledge on database management
• Database administrators, database developers or database programmers interested in new developments in the area

The book can also be used by tutors in courses such as the following:

• Principles of Database Management
• Database Modelling
• Database Systems
• Data Management
• Data Modelling

It can also be useful to universities working out degrees in e.g. Big Data & Analytics and Data Science.

Table of contents

Part 1: Databases and Database Design

• Chapter 1: Fundamental Concepts of Database Management
• Chapter 2: Architecture and Categorization of DBMSs
• Chapter 3: Conceptual Data Modeling
• Chapter 4: Organizational Aspects of Data Management

Part 2: Types of Database Systems

• Chapter 5: Legacy Databases
• Chapter 6: Relational Databases: The Relational Model
• Chapter 7: Relational Databases: Structured Query Language (SQL)
• Chapter 8: Object Oriented Databases and Object Persistence
• Chapter 9: Extended Relational Databases
• Chapter 10: XML Databases
• Chapter 11: NoSQL Databases

Part 3: Physical Data Storage, Transaction Management and Database Access

• Chapter 12: Physical File Organization and Indexing
• Chapter 13: Physical Database Organization
• Chapter 14: Basics of Transaction Management
• Chapter 15: Accessing Databases and Database APIs
• Chapter 16: Data Distribution and Distributed Transaction Management

Part 4: Data Warehousing, Data Governance and Data Analytics

• Chapter 17: Data Warehousing and Business Intelligence
• Chapter 18: Data Integration, Data Quality and Data Governance
• Chapter 19: Big Data
• Chapter 20: Analytics

What you will find on this site

After the book is released, readers will be able to work with an interactive environment to:

• Play around with SQL queries
• Play around with the MongoDB NoSQL database
• Play around with the Neo4j graph database

In addition, we will provide extra material, video lectures, slides, and maintain an errata list here as well.

About the authors

Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is author of the books Credit Risk Management: Basic Concepts, Analytics in a Big Data World and Fraud Analytics using Descriptive, Predictive and Social Network Techniques and teaches e-learning courses on Advanced Analytics in a Big Data World and Credit Risk Modeling. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and credit risk management strategy.

Wilfried Lemahieu is full professor and Vice Dean for Education at the Faculty of Economics and Business, KU Leuven, Belgium. His teaching includes Database Management, Enterprise Information Management and Management Informatics. His current research focuses on big data storage & analytics, data quality, business process management and service oriented architectures. His findings have been published in international journals such as Decision Support Systems, Applied Soft Computing, International Journal of Information Management and Data & Knowledge Engineering. He is also a frequent lecturer for both academic and business audiences and has an extensive track record in research collaborations with industry. See feb.kuleuven.be/wilfried.lemahieu for further details.

Seppe vanden Broucke is an assistant professor at the Faculty of Economics and Business, KU Leuven, Belgium. His research interests include business data mining and analytics, machine learning, process management, process mining. His work has been published in well-known international journals and presented at top conferences. Seppe’s teaching includes Advanced Analytics, Big Data and Information Management courses. He also frequently teaches for industry and business audiences. See seppe.net for further details.