Multi-dimensional performance analysis and monitoring using integrated performance spectra

Denisov, V., Fahland, D., & Van Der Aalst, W. M. P. (2020). Multi-dimensional performance analysis and monitoring using integrated performance spectra. In C. Di Ciccio (Ed.), Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020): Padua, Italy, October 4-9, 2020 (pp. 27-30). (CEUR Workshop Proceedings; Vol. 2703).


In process mining, basic descriptive statistics over observed events of event logs or streams, projected onto a process model, are typically used for performance analysis. The so-called performance spectrum is used for the fine-grained description of process performance over time, additionally revealing phenomena related to the behavior of multiple cases together. The performance spectrum computed from traces aligned with a process model allows performance analysis of processes with concurrency. However, performance spectra are used to describe performance only along the case level, leaving performance analysis and monitoring of other process dimensions out of scope. This paper presents an approach and tool combining a synchronous proclet system with a performance spectrum for multi-dimensional performance real-time monitoring and post-mortem analysis. While the tool is a proof-of-concept implementation, designed for analysis of the control-flow, resource and queue dimensions of logistic processes, the presented concepts are general.

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