StrProMCDD is a recently published work that detects concept drifts in event streams (see the figure below). StrProMCDD uses several model-based distance measures to detect these deviations using an adaptive window concept. In this assignment, we would like to compare the performance of this model-based approach with log-based stream clustering approaches that try to detect drifts in the log file without discovering the underlying process model. The assignment includes using existing event datasets with known concept drifts and/or generating own ones with special concept drifts.
Contact: Dr. ing. Marwan Hassani.
