Discovering Process Models from Uncertain Event Data

Abstract

Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform further process-centric analyses. In this paper, we consider uncertain event logs, where data is recorded together with explicit uncertainty information. We describe a technique to discover a directly-follows graph from such event data which retains information about the uncertainty in the process. We then present experimental results of performing inductive mining over the directly-follows graph to obtain models representing the certain and uncertain part of the process.

12 pages, 7 figures, 1 table, 9 references

References in corpus (1)

• Mining Uncertain Event Data in Process Mining

Cited by in corpus (5)

• Conformance Checking Over Stochastically Known Logs
• Efficient Construction of Behavior Graphs for Uncertain Event Data
• Probability Estimation of Uncertain Process Trace Realizations
• PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
• Efficient Time and Space Representation of Uncertain Event Data