Declarative process discovery with evolutionary computing

The field of process mining deals with the extraction of knowledge from event logs. One task within the area of process mining entails the discovery of process models to represent real-life behavior as observed in day-to-day business activities. A large number of such process discovery algorithms have been proposed during the course of the past decade, among which techniques to mine declarative process models (e.g. Declare and AGNEs Miner) as well as evolutionary based techniques (e.g. Genetic Miner and Process Tree Miner). In this paper, we present the initial results of a newly proposed evolutionary based process discovery algorithm which aims to discover declarative process models, hence combining these two classes (declarative and genetic) of discovery techniques. To do so, we herein use a language bias similar to the one found in AGNEs Miner to allow for the conversion from a set of declarative control-flow based constraints (determining the conditions which have to be satisfied to enable to execution of an activity) to a procedural process model, i.e. a Petri net, though this language bias can be extended to include data-based constraints as well.