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Process Discovery Using Localized Events

  • Wil M. P. van der AalstEmail author
  • Anna Kalenkova
  • Vladimir Rubin
  • Eric Verbeek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9115)

Abstract

Process mining techniques aim to analyze and improve conformance and performance of processes using event data. Process discovery is the most prominent process-mining task: A process model is derived based on an event log. The process model should be able to capture causalities, choices, concurrency, and loops. Process discovery is very challenging because of trade-offs between fitness, simplicity, precision, and generalization. Note that event logs typically only hold example behavior and cannot be assumed to be complete (to avoid overfitting). Dozens of process discovery techniques have been proposed. These use a wide range of approaches, e.g., language- or state-based regions, genetic mining, heuristics, expectation maximization, iterative log-splitting, etc. When models or logs become too large for analysis, the event log may be automatically decomposed or traces may be clustered before discovery. Clustering and decomposition are done automatically, i.e., no additional information is used. This paper proposes a different approach where a localized event log is assumed. Events are localized by assigning a non-empty set of regions to each event. It is assumed that regions can only interact through shared events. Consider for example the mining of software systems. The events recorded typically explicitly refer to parts of the system (components, services, etc.). Currently, such information is ignored during discovery. However, references to system parts may be used to localize events. Also in other application domains, it is possible to localize events, e.g., communication events in an organization may refer to multiple departments (that may be seen as regions). This paper proposes a generic process discovery approach based on localized event logs. The approach has been implemented in ProM and experimental results show that location information indeed helps to improve the quality of the discovered models.

Keywords

Visible Trace Conformance Check Vertical Decomposition Inductive Miner Unique Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wil M. P. van der Aalst
    • 1
    • 2
    Email author
  • Anna Kalenkova
    • 2
  • Vladimir Rubin
    • 2
  • Eric Verbeek
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.National Research University Higher School of EconomicsMoscowRussia

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