Concept Drift Detection of Event Streams Using an Adaptive Window

Process mining is an emerging data mining task of gathering valuable knowledge out of the huge collections of business operation data. Despite its relatively young age, it has successfully provided many new insights into business workflows using established data mining techniques. Recently, with the huge improvements in the technologies of sensoring, collection and storing of data, a big demand for both shorter mining times and adaptive models of streaming process events arose. This initiated the field of stream process mining very recently. Drifts in the underlying concepts of the business processes are of a great interest for decision makers. One important advantage of stream process mining techniques over static ones is the ability to detect such drifts and to adapt its models accordingly. In this paper, we introduce an efficient approach that uses the collected information of an event stream miner to detect concept drifts. We use a dynamic window, which grows in size for stationary process behavior and shrinks for diverting data and thus indicating a concept drift. This adaptive window is used to build a model by focusing only on up-to-date information and discarding outdated items. Extensive experimental evaluations over real and synthetic log files show the ability of our algorithm to detect sudden drifts. We additionally show the effectiveness of our concept detection method in setting the pruning period of a recent stream mining algorithm.