Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Detecting Branching Condition Changes in Process Models

Version 1 : Received: 9 September 2021 / Approved: 10 September 2021 / Online: 10 September 2021 (15:44:14 CEST)

How to cite: Lu, Y.; Chen, Q.; Poon, S.K. Detecting Branching Condition Changes in Process Models. Preprints 2021, 2021090191 (doi: 10.20944/preprints202109.0191.v1). Lu, Y.; Chen, Q.; Poon, S.K. Detecting Branching Condition Changes in Process Models. Preprints 2021, 2021090191 (doi: 10.20944/preprints202109.0191.v1).

Abstract

Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

Keywords

Process science; Data science; Concept drift detection and Branching frequency changes

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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