Discovering Workflow Changes with Time-Based Trace Clustering

Abstract : This paper proposes a trace clustering approach to support process discovery of configurable, evolving process models. The clustering approach allows auditors to distinguish between different process variants within a timeframe, thereby visualizing the process evolution. The main insight to cluster entries is the “distance” between activities, i.e. the number of steps between an activity pair. By observing non-transient modifications on the distance, changes in the original process shape can be inferred and the entries clustered accordingly. The paper presents the corresponding algorithms and exemplifies its usage in a running example.
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Communication dans un congrès
Karl Aberer; Ernesto Damiani; Tharam Dillon. 1st International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Jun 2011, Campione d’Italia, Italy. Springer, Lecture Notes in Business Information Processing, LNBIP-116, pp.154-168, 2012, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-642-34044-4_9〉
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Rafael Accorsi, Thomas Stocker. Discovering Workflow Changes with Time-Based Trace Clustering. Karl Aberer; Ernesto Damiani; Tharam Dillon. 1st International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Jun 2011, Campione d’Italia, Italy. Springer, Lecture Notes in Business Information Processing, LNBIP-116, pp.154-168, 2012, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-642-34044-4_9〉. 〈hal-01515551〉

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