TU Darmstadt / ULB / TUbiblio

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

Klinkmüller, Christopher ; Seeliger, Alexander ; Müller, Richard ; Pufahl, Luise ; Weber, Ingo
Polyvyanyy, Artem ; Wynn, Moe Thandar ; Van Looy, Amy ; Reichert, Manfred (eds.) (2021):
A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties.
In: Lecture Notes in Computer Science, 12875, In: Business Process Management, pp. 65-84,
Cham, Springer International Publishing, 19th International Conference on Business Process Management (BPM 2021), Rome, Italy, 06.09.-10.09.2021, ISBN 978-3-030-85469-0,
DOI: 10.1007/978-3-030-85469-0_7,
[Conference or Workshop Item]

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 its properties. Ultimately, not considering this possibility can negatively affect downstream decision making. We hence propose a framework for assessing the consistency of model properties 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.

Item Type: Conference or Workshop Item
Erschienen: 2021
Editors: Polyvyanyy, Artem ; Wynn, Moe Thandar ; Van Looy, Amy ; Reichert, Manfred
Creators: Klinkmüller, Christopher ; Seeliger, Alexander ; Müller, Richard ; Pufahl, Luise ; Weber, Ingo
Title: A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties
Language: English
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 its properties. Ultimately, not considering this possibility can negatively affect downstream decision making. We hence propose a framework for assessing the consistency of model properties 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.

Title of Book: Business Process Management
Series Name: Lecture Notes in Computer Science
Volume: 12875
Place of Publication: Cham
Publisher: Springer International Publishing
ISBN: 978-3-030-85469-0
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Event Title: 19th International Conference on Business Process Management (BPM 2021)
Event Location: Rome, Italy
Event Dates: 06.09.-10.09.2021
Date Deposited: 06 Sep 2021 07:35
DOI: 10.1007/978-3-030-85469-0_7
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details