105 citations · 1.4k across the 94 of their papers we have counts for
14 papers · 1 filter
Framework for Grouping Local Process Models
Viki Peeva, Wil M. P. van der Aalst
Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent year…
Grouping Local Process Models
Viki Peeva, Wil M. P. van der Aalst
In recent years, process mining emerged as a proven technology to analyze and improve operational processes. An expanding range of organizations using process mining in their daily…
Extracting Rules from Event Data for Study Planning
Majid Rafiei, Duygu Bayrak, Mahsa Pourbafrani +4
In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students. The main goal is to offer valuable guid…
Tailoring Machine Learning for Process Mining
Paolo Ceravolo, Sylvio Barbon Junior, Ernesto Damiani +1
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and…
TraVaG: Differentially Private Trace Variant Generation Using GANs
Majid Rafiei, Frederik Wangelik, Mahsa Pourbafrani +1
Process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorit…
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…
A Framework for Extracting and Encoding Features from Object-Centric Event Data
Jan Niklas Adams, Gyunam Park, Sergej Levich +2
Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-c…
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…
Feature Recommendation for Structural Equation Model Discovery in Process Mining
Mahnaz Sadat Qafari, Wil van der Aalst
Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root c…
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…
Mining Process Model Descriptions of Daily Life through Event Abstraction
Niek Tax, Natalia Sidorova, Reinder Haakma +1
Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contr…
Generating Time-Based Label Refinements to Discover More Precise Process Models
Niek Tax, Emin Alasgarov, Natalia Sidorova +2
Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data…
Heuristic Approaches for Generating Local Process Models through Log Projections
Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst +1
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. su…
Event Abstraction for Process Mining using Supervised Learning Techniques
Niek Tax, Natalia Sidorova, Reinder Haakma +1
Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery…