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Semantics-based event log aggregation for process mining and analytics

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Abstract

In highly complex and flexible environments, event logs tend to exhibit high levels of heterogeneity, and clustering-based methods are candidate techniques for simplifying the mined process models from the process observations. To compensate for the information loss occurring during clustering, semantic information from event logs may be extracted and organized in the form of knowledge structures such as process ontologies using methods of ontology learning. In this article, we propose an overall computational framework for event log pre-processing, and then focus on a specific component of the framework, namely event log aggregation. We develop a detailed system architecture for this component, along with an implemented and evaluated research prototype SemAgg. We use phrase-based semantic similarity between normalized event names to aggregate event logs in a hierarchical form. We discuss the practical implications of this work for learning lower level process ontology classes as well as performing further process mining and analytics.

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Correspondence to Amit V. Deokar.

Appendix

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Fig. 10
figure 10

Event name normalization algorithm

Fig. 11
figure 11

Similarities between event names from the experiment output

Fig. 12
figure 12

Similarities between event names generated by domain experts

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Deokar, A.V., Tao, J. Semantics-based event log aggregation for process mining and analytics. Inf Syst Front 17, 1209–1226 (2015). https://doi.org/10.1007/s10796-015-9563-4

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