This book delivers extensive coverage of data warehousing and business intelligence technologies, from basic principles to recent applications. It illustrates how all concepts can be implemented using Microsoft Analysis Services and Pentaho Business Analytics.

With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes Fundamental Concepts including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details Implementation and Deployment, which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers Advanced Topics such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases.

As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter.

Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.

Extensive coverage of all data warehouse issues, ranging from basic technologies to the most recent findings and systems Each chapter summarized by review questions and detailed exercises Concepts illustrated by an on going example based on the North wind database using Microsoft Analysis Services and Pentaho Business Analytics Additional website available with further teaching material, including slides for each chapter and solutions to all exercises Includes supplementary material: sn.pub/extras

Autorentext

Alejandro Vaisman is a professor at the Instituto Tecnológico de Buenos Aires, where he also chairs the graduate program in data science. He has been a professor and chair of the master's program in data mining at the University of Buenos Aires (UBA) and professor at Universidad de la República in Uruguay. His research interests are in the fields of relational databases, graph databases, business intelligence, and geographic information systems. Esteban Zimányi is a professor and Director of the Department of Computer and Decision Engineering at the Université Libre de Bruxelles. His current research interests include data warehouses and business intelligence, spatio-temporal and mobility databases, as well as the semantic Web. He coordinated the Erasmus Mundus master's and doctorate programmes "Information Technologies for Business Intelligence" (IT4BI) and "Big Data Management and Analytics" (BDMA) as well as the Marie Sk odowska-Curie doctorate programme "DataEngineering for Data Science" (DEDS).

Inhalt
Part I Fundamental Concepts.- 1 Introduction.- 2 Database Concepts.- 3 Data Warehouse Concepts.- 4 Conceptual Data Warehouse Design.- 5 Logical Data Warehouse Design.- 6 Querying Data Warehouses.- Part II Implementation and Deployment.- 7 Physical Data Warehouse Design.- 8 Extraction, Transformation and Loading.- 9 Data Analytics: Exploiting the Data Warehouse.- 10 A Method for Data Warehouse Design.- Part III Advanced Topics.- 11 Spatial Data Warehouses.- 12 Trajectory Data Warehouses.- 13 New Data Warehouse Technologies.- 14 Data Warehouses and the Semantic Web.- 15 Conclusion.