95 citations · 315 across the 14 of their papers we have counts for
15 papers
Performance-Preserving Event Log Sampling for Predictive Monitoring
Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park +3
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant intere…
Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data
Daniel Schuster, Michael Martini, Sebastiaan J. van Zelst +1
Event logs, as viewed in process mining, contain event data describing the execution of operational processes. Most process mining techniques take an event log as input and generat…
Conformance Checking for Trace Fragments Using Infix and Postfix Alignments
Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst +1
Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, loc…
Event Log Sampling for Predictive Monitoring
Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park +3
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant intere…
Visualizing Trace Variants From Partially Ordered Event Data
Daniel Schuster, Lukas Schade, Sebastiaan J. van Zelst +1
Executing operational processes generates event data, which contain information on the executed process activities. Process mining techniques allow to systematically analyze event…
Freezing Sub-Models During Incremental Process Discovery: Extended Version
Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Process discovery aims to learn a process model from observed process behavior. From a user's perspective, most discovery algorithms work like a black box. Besides parameter tuning…