Conformance checking

Abstract: Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.

Abstract: This is the second edition of Wil van der Aalsts 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.

Abstract: Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history using the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.