Fundamentals of Business Intelligence

Wilfried Grossmann is retired full professor for statistics at the Faculty of Informatics, University of Vienna. He has published in the areas of business informatics, medical informatics, data mining, operations research, applied statistics and statistical computing. His research focuses on the interface between applied statistics, statistical data management and metadata for statistical information systems. He was leader and key researcher of several European projects for the development of statistical information systems, statistical metadata management and data validation.Stefanie Rinderle-Ma is full professor and head of the research group Workflow Systems and Technology at the Faculty of Computer Science, University of Vienna. Stefanie’s research interests focus on business process compliance and intelligence, human-centered approaches in business process management (BPM,) and process-aware information systems (PAIS). She is co-author of more than 120 conference and journal publications with over 4600 citations (according to scholar.google.com) in the area of PAIS, BPM, and service-oriented architectures.

"The usage of examples and case studies enable real life application and brings asophisticated text to life. ... the book is a comprehensive and thoroughly well thought out introduction to the subject of business intelligence and the reader will not be left wanting as the clear examples are numerous. ... Readers interested in the value of data and the concepts behind business intelligence will find the book and its accompanying website highly informative." (Georgette Banham, bcs, The Chartered Institute for IT, bcs.org, August, 2016) "This book focuses primarily on the data mining, data warehousing, data analytics, data visualization, data presentation, and process analysis dimensions of BI in detail. ... One of the noteworthy strengths of this book is the inclusion of comprehensive lists with very recent and relevant references for BI at the end of each chapter. This should make the book very useful for academic research on the topic." (Satya Prakash Saraswat, Computing Reviews, February, 2016)

• 1 Introduction.- 2 Modeling in Business Intelligence.- 3 Data Provisioning.- 4 Data Description and Visualization.- 5 Data Mining for Cross-Sectional Data.- 6 Data Mining for Temporal Data.- 7 Process Analysis.- 8 Analysis of Multiple Business Perspectives.- 9 Summary.- A Survey on Business Intelligence Tools.