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WIREs Data Mining Knowl Discov
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Replaying history on process models for conformance checking and performance analysis

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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. This article is categorized under: Algorithmic Development > Association Rules Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Business and Industry Fundamental Concepts of Data and Knowledge > Data Concepts

One event log L (right) and four process models M1, M2, M3, and M4 (left).

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Fundamental Concepts of Data and Knowledge > Data Concepts
Algorithmic Development > Association Rules
Application Areas > Business and Industry
Algorithmic Development > Spatial and Temporal Data Mining

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