Revealing the Importance of Setting Parameters in Declarative Discovery Algorithms: An Evolutionary-Based Methodology

Abstract

Declarative process models represent the permitted behaviour associated with a business process by limiting the potential correct traces. These models can be discovered by analysing an event log. However, various declarative business models can be extracted from a single event log, depending on the desirable level of metrics, such as fitness and generalisation. Existing discovery algorithms enable the discovered declarative process model to be customised through a set of configuration parameters. Depending on the values of these parameters, the discovered process can be of high or low quality. Unfortunately, the high number of combinatorial parameters and the high time consumption of process discovery make it impractical to conduct an exhaustive analysis of the configuration parameters to determine the most suitable declarative process model discovered. As a solution, we propose a methodology supported by an implemented framework that uses evolutionary algorithms to reduce computational complexity and to select the highest quality declarative business processes. An experiment is included to show the feasibility of our proposal.