105 citations · 1.4k across the 94 of their papers we have counts for
127 papers
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…
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Marco Pegoraro, Jonas Seng, Dustin Heller +2
Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data. However, an obstac…
PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks
Alessandro Berti, Humam Kourani, Wil M. P. van der Aalst
Large Language Models (LLMs) have the potential to semi-automate some process mining (PM) analyses. While commercial models are already adequate for many analytics tasks, the compe…
Challenges of Anomaly Detection in the Object-Centric Setting: Dimensions and the Role of Domain Knowledge
Alessandro Berti, Urszula Jessen, Wil M. P. van der Aalst +1
Object-centric event logs, allowing events related to different objects of different object types, represent naturally the execution of business processes, such as ERP (O2C and P2P…
High-Level Event Mining: Overview and Future Work
Bianka Bakullari, Wil M. P. van der Aalst
Process mining traditionally relies on input consisting of low-level events that capture individual activities, such as filling out a form or processing a product. However, many of…
Process Variant Analysis Across Continuous Features: A Novel Framework
Ali Norouzifar, Majid Rafiei, Marcus Dees +1
Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only…
Process Modeling With Large Language Models
Humam Kourani, Alessandro Berti, Daniel Schuster +1
In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilit…
OCEL 2.0 Resources -- www.ocel-standard.org
Istvan Koren, Niklas Adams, Alessandro Berti
Process mining has become a cornerstone of process analysis and improvement over the last few years. With the widespread adoption of process mining tools and libraries, the limitat…
Developing a High-Performance Process Mining Library with Java and Python Bindings in Rust
Aaron Küsters, Wil M. P. van der Aalst
The most commonly used open-source process mining software tools today are ProM and PM4Py, written in Java and Python, respectively. Such high-level, often interpreted, programming…
Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review
Alessandro Berti, Marco Montali, Wil M. P. van der Aalst
Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from p…
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…
Analyzing An After-Sales Service Process Using Object-Centric Process Mining: A Case Study
Gyunam Park, Sevde Aydin, Cuneyt Ugur +1
Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object…
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…
The Interplay Between High-Level Problems and The Process Instances That Give Rise To Them
Bianka Bakullari, Jules van Thoor, Dirk Fahland +1
Business processes may face a variety of problems due to the number of tasks that need to be handled within short time periods, resources' workload and working patterns, as well as…
Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study
Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst
Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparab…
Applying Process Mining on Scientific Workflows: a Case Study on High Performance Computing Data
Zahra Sadeghibogar, Alessandro Berti, Marco Pegoraro +1
Computer-based scientific experiments are becoming increasingly data-intensive, necessitating the use of High-Performance Computing (HPC) clusters to handle large scientific workfl…
A Collection of Simulated Event Logs for Fairness Assessment in Process Mining
Timo Pohl, Alessandro Berti, Mahnaz Sadat Qafari +1
The analysis of fairness in process mining is a significant aspect of data-driven decision-making, yet the advancement in this field is constrained due to the scarcity of event dat…
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…
Object-Centric Alignments
Lukas Liss, Jan Niklas Adams, Wil M. P. van der Aalst
Processes tend to interact with other processes and operate on various objects of different types. These objects can influence each other creating dependencies between sub-processe…
Revisiting the Alpha Algorithm To Enable Real-Life Process Discovery Applications -- Extended Report
Aaron Küsters, Wil M. P. van der Aalst
The Alpha algorithm was the first process discovery algorithm that was able to discover process models with concurrency based on incomplete event data while still providing formal…
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…
Discovering Process Models that Support Desired Behavior and Avoid Undesired Behavior
Ali Norouzifar, Wil van der Aalst
Process discovery is one of the primary process mining tasks and starting point for process improvements using event data. Existing process discovery techniques aim to find process…
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…
Comparing Ordering Strategies For Process Discovery Using Synthesis Rules
Tsung-Hao Huang, Wil M. P. van der Aalst
Process discovery aims to learn process models from observed behaviors, i.e., event logs, in the information systems.The discovered models serve as the starting point for process m…