43 citations · 102 across the 11 of their papers we have counts for
12 papers
Discovering High-Quality Process Models Despite Data Scarcity
Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff +6
Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multipl…
Resolving Uncertain Case Identifiers in Interaction Logs: A User Study
Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann +1
Modern software systems are able to record vast amounts of user actions, stored for later analysis. One of the main types of such user interaction data is click data: the digital t…
Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data
Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann +1
Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting…
An XES Extension for Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
Event data, often stored in the form of event logs, serve as the starting point for process mining and other evidence-based process improvements. However, event data in logs are of…
Probability Estimation of Uncertain Process Trace Realizations
Marco Pegoraro, Bianka Bakullari, Merih Seran Uysal +1
Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which co…
Removing Operational Friction Using Process Mining: Challenges Provided by the Internet of Production (IoP)
Wil van der Aalst, Tobias Brockhoff, Anahita Farhang Ghahfarokhi +3
Operational processes in production, logistics, material handling, maintenance, etc., are supported by cyber-physical systems combining hardware and software components. As a resul…
Text-Aware Predictive Monitoring of Business Processes
Marco Pegoraro, Merih Seran Uysal, David Benedikt Georgi +1
The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction met…
PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
The discipline of process mining aims to study processes in a data-driven manner by analyzing historical process executions, often employing Petri nets. Event data, extracted from…
Efficient Time and Space Representation of Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance…
Conformance Checking over Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of proces…
Efficient Construction of Behavior Graphs for Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and no…
Discovering Process Models from Uncertain Event Data
Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst
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 o…