Mining Uncertain Event Data in Process Mining

Abstract

Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. We define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained aligning an uncertain trace onto a regular process model.

18 pages, 7 figures, 3 tables, 13 references

Cited by in corpus (7)

• Discovering Process Models from Uncertain Event Data
• Conformance Checking Over Stochastically Known Logs
• Efficient Construction of Behavior Graphs for Uncertain Event Data
• Probability Estimation of Uncertain Process Trace Realizations
• A Systematic Literature Review on Process-Aware Recommender Systems
• PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
• Efficient Time and Space Representation of Uncertain Event Data