Online comparison of streaming process discovery algorithms

Baskar, K., & Hassani, M. (2019). Online comparison of streaming process discovery algorithms. In B. Depaire, J. De Smedt , & M. Dumas (Eds.), Proceedings of the Dissertation Award, Doctoral Consortium, and Demonstration Track at BPM 2019 co-located with 17th International Conference on Business Process Management (BPM 2019) (pp. 164-168). (CEUR Workshop Proceedings; Vol. 2420).


In the active field of process mining, several techniques have been proposed in various areas like process discovery and conformance checking. The integration of data stream mining techniques in process mining has gained popularity in recent years. The ProM framework that enables process mining with streaming data has been advanced to support event streams in the recent past. In this paper we present a new extension that is built upon existing work related to obtaining process models from data streams within ProM. The extension enables researchers to visually compare the results of two different process discovery algorithms for a single incoming stream of events with different algorithms to deal with the data streams such as Lossy Counting with Budget, Sliding Window and Exponential Decay.

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