Journal ArticleDOI
Replaying history on process models for conformance checking and performance analysis
TLDR
In this paper, the authors focus on the importance of maintaining a proper alignment between event logs and process models and elaborate on the realization of such alignments and their application to conformance checking and performance analysis.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.read more
Citations
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Journal ArticleDOI
Business Process Management: A Comprehensive Survey
Wmp Wil van der Aalst,van der +1 more
TL;DR: The practical relevance of BPM and rapid developments over the last decade justify a comprehensive survey and an overview of the state-of-the-art in BPM.
Book ChapterDOI
Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour
TL;DR: In this paper, the authors present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness, which has been implemented in ProM and compared with existing approaches in terms of quality and performance.
Journal ArticleDOI
Predicting process behaviour using deep learning
TL;DR: This paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process, and shows results that surpass the state-of-the-art in prediction precision.
Journal ArticleDOI
Process Mining: Overview and Opportunities
TL;DR: This article introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto.
Book ChapterDOI
On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery
TL;DR: The quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness.
References
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Posted Content
Principles of data mining
TL;DR: This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.
Book
Process Mining: Discovery, Conformance and Enhancement of Business Processes
TL;DR: This book provides real-world techniques for monitoring and analyzing processes in real time and is a powerful new tool destined to play a key role in business process management.
Journal ArticleDOI
Workflow mining: discovering process models from event logs
TL;DR: A new algorithm is presented to extract a process model from a so-called "workflow log" containing information about the workflow process as it is actually being executed and represent it in terms of a Petri net.
Book
Business Process Management: Concepts, Languages, Architectures
TL;DR: Matthias Weske argues that all communities involved need to have a common understanding of the different aspects of business process management, and details the complete business process lifecycle from the modeling phase to process enactment and improvement, taking into account all different stakeholders involved.
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