Unraveling and Learning Workflow Models from Interleaved Event Logs

Business process mining is to extract process knowledge from a system's log in order to reconstruct workflow models. Existing approaches treat a log record as an instance of one workflow model. They do not deal with interleaved logs, where each log record is a mixture of multiple workflow traces. However, such an interleaved log is typical for many systems especially web-based ones where all the user-system interaction traces are recorded and maintained by a web server. Dealing with interleaved logs is challenging due to the lack of prior knowledge of workflow models and noises contained in the log data. In this paper, we propose a two-phase workflow learning process. During the first phase, we use a probabilistic approach to learn the links between operations and the hidden workflow models. We consider a workflow model as a probabilistic distributions over operations and derive it through likelihood maximization. This allows us to identify the membership of an operation to a workflow model, which can be used to unravel a log record and generate a set of workflow instances from it. During the second phase, the sequential patterns between operations within each workflow model are derived from all its instances. We have conducted a comprehensive experimental study, which indicates the effectiveness of the proposed solution.