Measuring the interestingness of temporal logic behavioral specifications in process mining

Cecconi, Alessio ORCID: https://orcid.org/0000-0001-5730-6332 and De Giacomo, Giuseppe and Di Ciccio, Claudio ORCID: https://orcid.org/0000-0001-5570-0475 and Maggi, Fabrizio Maria and Mendling, Jan (2021) Measuring the interestingness of temporal logic behavioral specifications in process mining. Information Systems. ISSN 0306-4379

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Abstract

The assessment of behavioral rules with respect to a given dataset is key in several research areas, including declarative process mining, association rule mining, and specification mining. An assessment is required to check how well a set of discovered rules describes the input data, and to determine to what extent data complies with predefined rules. Particularly in declarative process mining, Support and Confidence are used most often, yet they are reportedly unable to provide a sufficiently rich feedback to users and cause rules representing coincidental behavior to be deemed as representative for the event logs. In addition, these measures are designed to work on a predefined set of rules, thus lacking generality and extensibility. In this paper, we address this research gap by developing a measurement framework for temporal rules based on (LTLp). The framework is suitable for any temporal rules expressed in a reactive form and for custom measures based on the probabilistic interpretation of such rules. We show that our framework can seamlessly adapt well-known measures of the association rule mining field to declarative process mining. Also, we test our software prototype implementing the framework on synthetic and real-world data, and investigate the properties characterizing those measures in the context of process analysis.

Item Type:	Article
Additional Information:	Article in Press. The work of Claudio Di Ciccio was partly supported by the Italian Ministry of University and Research (MUR) under grant “Dipartimenti di eccellenza 2018-2022” of the Department of Computer Science at Sapienza University of Rome and by the Sapienza SPECTRA research project, Italy. The research by Jan Mendling was supported by the Einstein Foundation Berlin.
Keywords:	Declarative process mining, Specification mining, Association rule mining, Interestingness measures, Temporal rules
Divisions:	Departments > Wirtschaftsinformatik u. Operations Mgmt > Data, Process and Knowledge Management > Mendling
Version of the Document:	Published
Depositing User:	Gertraud Novotny
Date Deposited:	22 Nov 2021 11:38
Last Modified:	22 Nov 2021 11:38
Related URLs:	Publisher
FIDES Link:	https://bach.wu.ac.at/d/research/results/102102/
URI:	https://epub.wu.ac.at/id/eprint/8419

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