A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties

EasyChair Preprint 5841

Abstract

Event logs have become a valuable information source for business process management, e.g., when analysts discover process models to inspect the process behavior and to infer actionable insights. To this end, analysts configure discovery pipelines in which logs are filtered, enriched, abstracted, and process models are derived. While pipeline operations are necessary to manage log imperfections and complexity, they might, however, influence the nature of the discovered process model and hence the inferred insights. Ultimately, not considering this possibility can negatively affect downstream decision making. We hence propose a framework for assessing the consistency of process discovery insights with respect to the pipeline operations and their parameters, and, if inconsistencies are present, for revealing which parameters contribute to them. Following recent literature on software engineering for machine learning, we refer to it as debugging. From evaluating our framework in a real-world analysis scenario based on complex event logs and third-party pipeline configurations, we see strong evidence towards it being a valuable addition to the process mining toolbox.

Keyphrases: Process Discovery, Process Mining, Uncertainty and Sensitivity Analysis

@booklet{EasyChair:5841,
  author    = {Christopher Klinkmüller and Alexander Seeliger and Richard Müller and Luise Pufahl and Ingo Weber},
  title     = {A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties},
  howpublished = {EasyChair Preprint 5841},
  year      = {EasyChair, 2021}}