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.
