A machine learning approach to workflow management

Abstract

There has recently been some interest in applying machine learning techniques to support the acquisition and adaptation of workflow models. The different learning algorithms, that have been proposed, share some restrictions, which may prevent them from being used in practice. Approaches applying techniques from grammatical inference are restricted to sequential workflows. Other algorithms allowing concurrency require unique activity nodes. This contribution shows how the basic principle of our previous approach to sequential workflow induction can be generalized, so that it is able to deal with concurrency. It does not require unique activity nodes. The presented approach uses a log-likelihood guided search in the space of workflow models, that starts with a most general workflow model containing unique activity nodes. Two split operators are available for specialization.