A framework for detecting deviations in complex event logs

Deviating behavior within an organization can lead to unexpected results. The effects of deviations are often negative, but sometimes also positive. Therefore, it is useful to detect deviations from event logs which record all the behavior of the organization. However, existing model-based and cluster-based approaches are inaccurate or slow when dealing with complex event logs, i.e. logs of less structured processes having many activities and many possible paths. This paper proposes a novel approach that is faster than cluster-based approaches because it creates a so-called profile which is less time-consuming than creating clusters. Furthermore, the approach is also more accurate than model-based approaches because we use an iterative approach to improve the result. Our experiments show that approach outperforms existing techniques in a variety of circumstances.