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Process Histories - Detecting and Representing Concept Drifts Based on Event Streams

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 11229)

Abstract

Business processes have to constantly adapt in order to react to changes induced by, e.g., new regulations or customer needs resulting in so called concept drifts. By now techniques to detect concept drifts are applied on process execution logs ex post, i.e., after the process is finished. However, detecting concept drifts during run-time bears many benefits such as instant reaction to the concept drift. Introducing process histories as a novel way to detect and represent incremental, sudden, recurring, and gradual concept drifts through mining the evolution of a process model based on an event stream will face this challenge. Therefore, a formal definition of process histories is given, the concept of process histories is prototypically implemented and compared with existing approaches based on a synthetic event log.

Keywords

  • Process mining
  • Event streams
  • Runtime
  • Concept drift
  • Process histories

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Notes

  1. 1.

    Extensible Event Stream.

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Acknowledgment

This work has been funded by the Vienna Science and Technology Fund (WWTF) through project ICT15-072.

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Correspondence to Florian Stertz .

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Stertz, F., Rinderle-Ma, S. (2018). Process Histories - Detecting and Representing Concept Drifts Based on Event Streams. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-02610-3_18

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