Time-aware Concept Drift Detection Using the Earth Mover’s Distance

International Conference on Process Mining (ICPM), pp. 33–40

Abstract

Modern business processes are embedded in a complex environment and, thus, subjected to continuous changes. While current approaches focus on the control flow only, additional perspectives, such as time, are neglected. In this paper, we investigate a more general concept drift detection framework that is based on the Earth Mover's Distance. Our approach is flexible in terms of incorporating additional perspectives thanks to the capability of defining custom feature representations, as well as expressive feature similarity measures. We demonstrate the former by incorporating the time perspective using both a time-binning-based trace descriptor and a suitable similarity measure that considers time and control flow. We evaluate the resulting sliding window detector on different types of control-flow and time drifts, and holistic drifts involving multiple perspectives.

Authors 3

1. Tobias Brockhoff Aachen Process and Data Science Group (PADS) Computer Sience 9

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group (PADS) Computer Sience 9, RWTH Aachen University, Aachen, Germany

2. Merih Seran Uysal Aachen Process and Data Science Group (PADS) Computer Sience 9

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group (PADS) Computer Sience 9, RWTH Aachen University, Aachen, Germany

3. Wil M. P. van der Aalst Aachen Process and Data Science Group (PADS) Computer Sience 9

   RWTH Aachen University

   Affiliation as printed

   Process and Data Science Group (PADS) Computer Sience 9, RWTH Aachen University, Aachen, Germany