A Machine Learning Approach to Work owManagementJoachim

There has recently been some interest in applying machine learning techniques to support the acquisition and adaptation of work-ow models. The diierent 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 workkows. Other algorithms allowing con-currency require unique activity nodes. This contribution shows how the basic principle of our previous approach to sequential workkow 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 workkow models, that starts with a most general workkow model containing unique activity nodes. Two split operators are available for specialization.