In process mining, event logs of processes are analyzed in order to extract information about the organization, processes, and products. For example, a business process workflow for giving credit to customers at a bank is run multiple times throughout the day. Event logs are created from the workflow executions. When process mining algorithms inspect event logs, valuable information can be extracted such as how much it costs for a bank to decide on a bank loan. If bank loan requests from people under a certain credit score are always rejected, it might be better not to consider these applications to save money and time for the bank. There are certain algorithms and techniques for doing such process mining tasks. The book explains these methods.

The book contains 14 chapters. The first chapter gives basic information about process models and event logs. Chapter 2 is about different ways of doing analysis and modeling processes. Chapter 3 presents preliminary information about data mining such as supervised and unsupervised learning models. Chapter 4 presents ways of getting data from process management systems. Chapters 5 and 6 give information about process discovery. Process discovery is the first step of building a process model. Real-life events are inspected to build models, and there are certain methods for discovering processes. Once a model is built, mining is done over process history. Chapter 7 is about conformance checking, which is assuring the coherency between processes and event logs. Chapter 8 lists the additional perspectives on process mining such as decision mining, replaying a model to find out the unused patterns. Chapter 9, “Operational Support,” presents information about predictions on process models. Predictions are important for making decisions. Chapter 10 lists open-source tools such as ProM for doing process mining. Chapters 11 and 12 are about analyzing processes and best practices in operational management. Chapter 13 gives a navigation example to illustrate a real-world problem, and chapter 14 concludes the book.

This topic is important for the following reasons. Big data and data analytics are going to shape how business is done in the future. Process mining is closely tied to data analytics. In big data analytics, there is raw data. In process mining, we deal with huge event logs.

The author of the book, Wil van der Aalst, is very knowledgeable in the area. He is a full professor of information systems at the Technische Universiteit Eindhoven. His research interests are workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. He has published many journal papers, books, refereed conference/workshop publications, and book chapters on these topics. He also has a course on Coursera. I enjoyed reading the book and learned about process mining.

It will be helpful to researchers and industry professionals working on fields related to business processes such as business intelligence and workflow management. This subject can also be a topic for graduate studies because there are still many open problems.