Principles of Database Management The Practical Guide to Storing, Managing and Analyzing Big and Small Data

This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.

Preface; Part I. Databases and Database Design: 1. Fundamental concepts of database management; 2. Architecture and categorization of DBMSs; 3. Conceptual data modeling using the (E)ER model and UML class diagram; 4. Organizational aspects of data management; Part II. Types of Database Systems: 5. Legacy databases; 6. Relational databases: the relational model; 7. Relational databases: structured query language (SQL); 8. Object oriented databases and object persistence; 9. Extended relational databases; 10. XML databases; 11. NoSQL databases; Part III. Physical Data Storage, Transaction Management, and Database Access: 12. Physical file organization and indexing; 13. Physical database organization; 14. Basics of transaction management; 15. Accessing databases and database APIs; 16. Data distribution and distributed transaction management; Part IV. Data Warehousing, Data Governance and (Big) Data Analytics: 17. Data warehousing and business intelligence; 18. Data integration, data quality and data governance; 19. Big data; 20. Analytics; Appendix A. Cases and questions; Appendix B. Using the online environment; Appendix C. Answer key to select review questions; Glossary; Index.

Wilfried Lemahieu is a professor at KU Leuven, Belgium, Faculty of Economics and Business, where he also holds the position of Dean. His teaching, for which he was awarded a 'best teacher recognition' includes courses on database management, enterprise information management and management informatics. His research focuses on big data storage and integration, data quality, business process management and service-oriented architectures. In this context, he collaborates extensively with a variety of industry partners, both local and international. His research is published in renowned international journals and he is a frequent lecturer for both academic and industry audiences.
Seppe vanden Broucke works as 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 and process mining. His work has been published in well-known international journals and presented at top conferences. He is also author of the book Beginning Java Programming (2015) of which more than 4000 copies were sold and which was also translated in Russian. Seppe's teaching includes advanced analytics, big data and information management courses. He also frequently teaches for industry and business audiences.
Bart Baesens is a professor of Big Data and Analytics at KU Leuven, Belgium and a lecturer at the University of Southampton. He has done extensive research on big data and analytics, credit risk modeling, fraud detection and marketing analytics. He has written more than 200 scientific papers some of which have been published in well-known international journals (e.g. MIS Quarterly, Machine Learning, Management Science, MIT Sloan Management Review and IEEE Transactions on Knowledge and Data Engineering) and presented at international top conferences (e.g. ICIS, KDD, CAISE). He received various best paper and best speaker awards. Bart is the author of 6 books: Credit Risk Management: Basic Concepts (2009), Analytics in a Big Data World (2014), Beginning Java Programming (2015), Fraud Analytics using Descriptive, Predictive and Social Network Techniques (2015), Credit Risk Analytics (2016) and Profit-Driven Business Analytics (2017). He sold more than 15.000 copies of these books worldwide, some of which have been translated in Chinese, Russian and Korean.