Fundamentals of Business Intelligence

  • Wilfried Grossmann
  • Stefanie Rinderle-Ma

Part of the Data-Centric Systems and Applications book series (DCSA)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 1-33
  3. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 35-85
  4. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 87-118
  5. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 119-154
  6. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 155-205
  7. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 207-244
  8. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 245-274
  9. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 275-317
  10. Wilfried Grossmann, Stefanie Rinderle-Ma
    Pages 319-327
  11. Back Matter
    Pages 329-348

About this book


This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques, and the combination of these methods for process-oriented data.

After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described, and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided.

The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.


BPM OLAP big data business intelligence business process management data mining data warehouses decision support systems online analytical processing process mining

Authors and affiliations

  • Wilfried Grossmann
    • 1
  • Stefanie Rinderle-Ma
    • 2
  1. 1.University of ViennaViennaAustria
  2. 2.University of ViennaViennaAustria

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-662-46530-1
  • Online ISBN 978-3-662-46531-8
  • Series Print ISSN 2197-9723
  • Series Online ISSN 2197-974X
  • Buy this book on publisher's site