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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22666

Title: Measuring the Quality of Models with Respect to the Underlying System: An Empirical Study
Authors: Janssenswillen, Gert
Jouck, Toon
Creemers, Mathijs
Depaire, Benoît
Issue Date: 2016
Publisher: Springer
Citation: La Rosa, Marcello; Loos, Peter; Pastor, Oscar (Ed.). Business Process Management: 14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18-22, 2016. Proceedings, Springer,p. 73-89
Series/Report: Lecture Notes in Computer Science (LNCS)
Series/Report no.: 9850
Abstract: Fitness and precision are two widely studied criteria to determine the quality of a discovered process model. These metrics measure how well a model represents the log from which it is learned. However, often the goal of discovery is not to represent the log, but the underlying system. This paper discusses the need to explicitly distinguish between a log and system perspective when interpreting the fitness and precision of a model. An empirical analysis was conducted to investigate whether the existing log-based fitness and precision measures are good estimators for system-based metrics. The analysis reveals that incompleteness and noisiness of event logs significantly impact fitness and precision measures. This makes them biased estimators of a model’s ability to represent the true underlying process.
Notes: Janssenswillen, G (reprint author), Hasselt Univ, Agoralaan Bldg D, B-3590 Diepenbeek, Belgium. gert.janssenswillen@uhasselt.be; toon.jouck@uhasselt.be; mathijs.creemers@uhasselt.be; benoit.depaire@uhasselt.be
URI: http://hdl.handle.net/1942/22666
DOI: 10.1007/978-3-319-45348-4_5
ISI #: 000388721400005
ISBN: 9783319453477
ISSN: 0302-9743
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2017
Appears in Collections: Research publications

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