This book provides a comprehensive, structured, and accessible resource that covers both foundational aspects and advanced topics of predictive process monitoring (PPM). It introduces the key building blocks of PPM from preliminary notions and core libraries to bucketing and encoding strategies, learning methods, and validation techniques. At the same time, the book extends its reach to advanced themes such as neuro-symbolic PPM, explainability, multi-modal predictive monitoring, and prescriptive approaches. This dual scope makes it both an introductory text and a reference work for advanced study.
The presentation is organized in seven chapters. Chapter 1 introduces the reader to the field, including its preliminaries and a helicopter view of PPM. Next, chapter 2 presents the tools and libraries that support implementation. Chapters 3 and 4 then delve into core data preparation aspects: prefix generation, bucketing, and encoding techniques. Chapter 5 discusses learning approaches, while Chapter 6 focuses on validation and testing. Finally, Chapter 7 highlights advanced topics that represent the current frontier of the field. Each chapter is enriched with exercises to facilitate learning and with notes to provide further reading.
This book mainly aims at graduate students and researchers in computer science, information systems, and data science who wish to gain a deep understanding of PPM. It is also designed for educators, who will find the structured exposition, exercises, and references suitable for designing and teaching courses on process mining and predictive analytics. Eventually, practitioners and professionals in industry will benefit from the guidance on applying PPM techniques to optimize and innovate their organizational processes.
