Quotation Di Ciccio, Claudio, Maggi , Fabrizio Maria , Mendling, Jan. 2016. Efficient discovery of Target-Branched Declare constraints. Information Systems (IS) 56, 258-283.




Process discovery is the task of generating process models from event logs. Mining processes that operate in an environment of high variability is an ongoing research challenge because various algorithms tend to produce spaghetti-like process models. This is particularly the case when procedural models are generated. A promising direction to tackle this challenge is the usage of declarative process modelling languages like Declare, which summarise complex behaviour in a compact set of behavioural constraints on activities. A Declare constraint is branched when one of its parameters is the disjunction of two or more activities. For example, branched Declare can be used to express rules like “in a bank, a mortgage application is always eventually followed by a notification to the applicant by phone or by a notification by e-mail”. However, branched Declare constraints are expensive to be discovered. In addition, it is often the case that hundreds of branched Declare constraints are valid for the same log, thus making, again, the discovery results unreadable. In this paper, we address these problems from a theoretical angle. More specifically, we define the class of Target-Branched Declare constraints and investigate the formal properties it exhibits. Furthermore, we present a technique for the efficient discovery of compact Target-Branched Declare models. We discuss the merits of our work through an evaluation based on a prototypical implementation using both artificial and real-life event logs.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Information Systems (IS)
Citation Index SCI
WU Journalrating 2009 A
WU-Journal-Rating new INF-A, STRAT-B, WH-B
Language English
Title Efficient discovery of Target-Branched Declare constraints
Volume 56
Year 2016
Page from 258
Page to 283
Reviewed? Y
URL http://www.sciencedirect.com/science/article/pii/S0306437915001271
DOI http://dx.doi.org/10.1016/j.is.2015.06.009


European Wide Service Platform for Green European Transportation
Di Ciccio, Claudio (Former researcher)
Mendling, Jan (Details)
Maggi , Fabrizio Maria (University of Tartu, Estonia)
Institute for Data, Process and Knowledge Management (AE Sabou) (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1108 Informatics (Details)
1109 Information and data processing (Details)
1127 Information science (Details)
5306 Business data processing (Details)
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