Mining real-life event logs results into process models which provide little value to the process analyst without support for handling complexity. Filtering techniques are specifically helpful to tackle this problem. These techniques have been focusing on leaving out infrequent aspects of the process which are considered outliers. However, it is exactly in these outliers where it is possible to gather important insights on the process. This paper addresses this problem by defining multi-range filtering. Our technique not only allows to combine both frequent and non-frequent aspects of the process but it supports any user-defined intervals of frequency of activities and variants. We evaluate our approach through a prototype based on the PM4Py library and show the benefits in comparison to existing filtering techniques.
|Title of host publication||Enterprise, Business-Process and Information Systems Modeling 21st International Conference, BPMDS 2020, 25th International Conference, EMMSAD 2020, Held at CAiSE 2020, Grenoble, France, June 8–9, 2020, Proceedings|
|Editors||Selmin Nurcan, Iris Reinhartz-Berger, Pnina Soffer, Jelena Zdravkovic|
|Place of Publication||Grenoble|
|Publisher||Springer Lecture Notes in Business Information Processing (LNBIP)|
|Pages||135 - 149|
|Publication status||Published - 2020|