Discovery of frequent episodes in event logs

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5 Citations (Scopus)
195 Downloads (Pure)


Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.
Original languageEnglish
Title of host publication4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014, Milan, Italy, November 19-21, 2014)
EditorsR. Accorsi, P. Ceravolo, B. Russo
Publication statusPublished - 2014
Event4th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2014) - Milan, Italy
Duration: 19 Nov 201421 Nov 2014
Conference number: 4

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


Conference4th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2014)
Abbreviated titleSIMPDA2014


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