Discovering Business Process Model from Unstructured Activity Logs

Abstract

Many real world business processes are executed without explicit orchestration and hence do not generate structured execution logs. This is particularly true for the class of business processes which are executed in service delivery centers in emerging markets where rapid changes in processes and in the people executing the processes are common. In such environments, the process execution logs are usually a mix of human entered activity log of actions performed and the auto-generated logs by various tools used during the process execution. Process discovery from unstructured execution logs has been a relatively unexplored research area. In this paper, we propose an approach for process discovery from unstructured logs using gaussian mixture models and hidden markov models. We apply this approach to the logs generated by a real-world business process used in a service delivery center and demonstrate that the results obtained are comparable to an approach of manually labeling the logs followed by a best known process discovery algorithm in literature. The approach proposed is generic and applicable to a wide range of business process execution settings.