Process Mining - Wil M. P. van der Aalst

Process Mining

Data Science in Action
Buch | Softcover
XIX, 467 Seiten
2018 | 2. Softcover reprint of the original 2nd ed. 2016
Springer Berlin (Verlag)
978-3-662-57041-8 (ISBN)
53,49 inkl. MwSt
The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool destined to play a key role in business process management.

This is the second edition of Wil van der Aalst's seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics.

After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. 

Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

Wil van der Aalst is a full professor at the Department of Mathematics & Computer Science of the Technische Universiteit Eindhoven (TU/e), The Netherlands, where he chairs the Architecture of Information Systems (AIS) group and serves as the scientific director of the Data Science Center Eindhoven. He also has a part-time appointment in the BPM group of Queensland University of Technology (QUT), Australia. His research and teaching interests include information systems, business process management, process modeling, Petri nets, process mining, and simulation. Wil has published more than 180 journal papers, 19 books, 425 refereed conference or workshop publications, and 60 book chapters. Many of his papers are highly cited (he has a H-index of more than 123 according to Google Scholar, the highest among all European computer scientists) and his ideas on process support have influenced researchers, software developers, and standardization committees worldwide.

Introduction.- Preliminaries.- From Event Logs to Process Models.- Beyond Process Discovery.- Putting Process Mining to Work.- Reflection.- Epilogue.

"The author of the book, Wil van der Aalst, is very knowledgeable in the area. ... His research interests are workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. ... 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." (Gulustan Dogan, Computing Reviews, March, 2017)

Erscheinungsdatum
Zusatzinfo XIX, 467 p. 250 illus., 13 illus. in color.
Verlagsort Berlin
Sprache englisch
Maße 155 x 235 mm
Gewicht 7314 g
Themenwelt Mathematik / Informatik Informatik Datenbanken
Mathematik / Informatik Informatik Software Entwicklung
Schlagworte Big Data • Business Information Systems • Business Intelligence • business process management • Data Mining • Data Science • Workflow Management
ISBN-10 3-662-57041-6 / 3662570416
ISBN-13 978-3-662-57041-8 / 9783662570418
Zustand Neuware
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