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.
The study of business processes has become a vital method for coordinating complex service- and knowledge-based activities within organizations. Business process management (BPM) focuses on the methods and tools for designing, enacting, and analyzing these processes. This volume serves as a definitive guide to using patterns that synthesize various approaches to modeling business processes. It introduces the well-known workflow patterns collection, which consists of recurrent, generic constructs that describe common scenarios in business process modeling and execution through problem-solution dialectics. The principles behind the patterns approach ensure independence from specific technologies, representational formalisms, or modeling methods, making them widely applicable across BPM domains. The authors, informed by extensive research from the Workflow Patterns Initiative, provide a thorough introduction to business process modeling and management fundamentals, detail three major pattern catalogs from control-flow, data, and resource perspectives, and survey related BPM patterns. This book, a companion to the authoritative Workflow Patterns website, is an essential resource for academics and practitioners in business process modeling and management.
This book offers a comprehensive introduction to workflow management, the management of business processes with information technology. By defining, analyzing, and redesigning an organization's resources and operations, workflow management systems ensure that the right information reaches the right person or computer application at the right time. The book provides a basic overview of workflow terminology and organization, as well as detailed coverage of workflow modeling with Petri nets. Because Petri nets make definitions easier to understand for nonexperts, they facilitate communication between designers and users. The book includes a chapter of case studies, review exercises, and a glossary.
This second edition of Wil van der Aalst’s influential work on process mining expands its scope to include data science and big data. It features updates on inductive mining techniques, alignments, an expanded section on software tools, and a new chapter on large-scale process mining. The book is self-contained, covering the full spectrum of process mining from discovery to predictive analytics. Part I introduces data science and process mining, while Part II lays the groundwork in business process modeling and data mining. Part III emphasizes process discovery, the key task in process mining, and Part IV explores conformance checking along with organizational and time perspectives. Part V guides readers in practical applications of process mining, introducing the widely used open-source tool ProM and various commercial products. Finally, Part VI reflects on the presented material and identifies key open challenges. This comprehensive overview serves business process analysts, consultants, managers, graduate students, and BPM researchers, offering valuable insights into the current state of process mining.