Skeletal algorithms in process mining

Abstract

This paper1studies sample applications of skeletal algorithm to process mining and automata discovery. The basic idea behind the skeletal algorithm is to express a problem in terms of congruences on a structure, build an initial set of congruences, and improve it by taking limited unions/intersections, until a suitable condition is reached. Skeletal algorithms naturally arise in the context of process minig and automata discovery, where the skeleton is the "free" structure on initial data and a congruence corresponds to similarities in data. In such a context, skeletal algorithms come equipped with fitness functions measuring the complexity of a model.We examine two fitness functions for our sample problem-one based on Minimum Description Length Principle, and the other based on Bayesian Interpretation. © Springer-Verlag Berlin Heidelberg 2013.