A Primer on Process Mining

The main goal of this book is to explain the core ideas of process mining, and to demonstrate how they can be implemented using just some basic tools that are available to any computer scientist or data scientist. It describes how to analyze event logs in order to discover the behavior of real-world business processes. The end result can often be visualized as a graph, and the book explains how to use Python and Graphviz to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently of any specific process mining tool. An introduction to two popular process mining tools, namely Disco and ProM, is also provided. In this second edition the code snippets have been updated to Python 3, and some smaller errors have been corrected.
The book will be especially valuable for self-study or as a precursor to a more advanced text. Practitioners and students will be able to follow along on their own, even if they have no prior knowledge of the topic. After reading this book, they will be able to more confidently proceed to the research literature if needed.

Explains the fundamental ideas of process mining in simple and practical terms Illustrates the approach by using two widely available tools, Python and Graphviz Intended for practitioners and students alike, for self-study or as a precursor to a more advanced text

Autorentext

Diogo R. Ferreira is Professor of Information Systems at the University of Lisbon, where he specializes on process mining, data analysis, and systems integration. He has been recognized several times for his pedagogical approach while teaching those subjects to computer science and other engineering students. He has supervised about thirty graduate students, and is the author of numerous publications. He has a particular interest in understanding processes (just about any kind of process) from the analysis of real-world event data.

Inhalt
Event Logs.- Control-Flow Perspective.- Organizational Perspective.- Performance Perspective.- Process Mining in Practice.