Efficient discovery of Target-Branched Declare constraints. (March 2016) - Di Ciccio, Claudio; Maggi, Fabrizio Maria; Mendling, Jan
Abstract: 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.