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.
