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https://hdl.handle.net/2440/137204
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Type: | Conference paper |
Title: | A framework for estimating simplicity of automatically discovered process models based on structural and behavioral characteristics |
Author: | Kalenkova, A. Polyvyanyy, A. La Rosa, M. |
Citation: | Lecture Notes in Artificial Intelligence, 2020 / Fahland, D., Ghidini, C., Becker, J., Dumas, M. (ed./s), vol.12168 LNCS, pp.129-146 |
Publisher: | Springer International Publishing |
Publisher Place: | New York, NY, USA |
Issue Date: | 2020 |
Series/Report no.: | Lecture Notes in Computer Science |
ISBN: | 9783030586652 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | Business Process Management International Conference (BPM) (13 Sep 2020 - 18 Sep 2020 : Seville, Spain) |
Editor: | Fahland, D. Ghidini, C. Becker, J. Dumas, M. |
Statement of Responsibility: | Anna Kalenkova, Artem Polyvyanyy, Marcello La Rosa |
Abstract: | A plethora of algorithms for automatically discovering process models from event logs has emerged. The discovered models are used for analysis and come with a graphical flowchart-like representation that supports their comprehension by analysts. According to the Occam’s Razor principle, a model should encode the process behavior with as few constructs as possible, that is, it should not be overcomplicated without necessity. The simpler the graphical representation, the easier the described behavior can be understood by a stakeholder. Conversely, and intuitively, a complex representation should be harder to understand. Although various conformance checking techniques that relate the behavior of discovered models to the behavior recorded in event logs have been proposed, there are no methods for evaluating whether this behavior is represented in the simplest possible way. Existing techniques for measuring the simplicity of discovered models focus on their structural characteristics such as size or density, and ignore the behavior these models encoded. In this paper, we present a conceptual framework that can be instantiated into a concrete approach for estimating the simplicity of a model, considering the behavior the model describes, thus allowing a more holistic analysis. The reported evaluation over real-life event logs for several instantiations of the framework demonstrates its feasibility in practice. |
Rights: | © 2020 Springer Nature Switzerland AG |
DOI: | 10.1007/978-3-030-58666-9_8 |
Grant ID: | http://purl.org/au-research/grants/arc/DP180102839 |
Published version: | https://www.springer.com/gp |
Appears in Collections: | Computer Science publications |
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