105 citations · 158 across the 16 of their papers we have counts for
16 papers
Inductive Process Discovery from Partially Ordered Event Data
Humam Kourani, Tom Breuer, Gyunam Park +1
The Inductive Miner (IM) family is a prominent class of process discovery techniques, combining efficient recursive decomposition with soundness-by-construction guarantees. However…
Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text
Anton Antonov, Humam Kourani, Alessandro Berti +1
Process modeling is a sub-domain of Business Process Management (BPM) focused on the translation of process artifacts into formal models. This task traditionally requires extensive…
PMAx: An Agentic Framework for AI-Driven Process Mining
Anton Antonov, Humam Kourani, Alessandro Berti +2
Process mining provides powerful insights into organizational workflows, but extracting these insights typically requires expertise in specialized query languages and data science…
Hierarchical Decomposition of Separable Workflow-Nets
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. While early versions…
Integrating Domain Knowledge into Process Discovery Using Large Language Models
Ali Norouzifar, Humam Kourani, Marcus Dees +1
Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvem…
Revealing Inherent Concurrency in Event Data: A Partial Order Approach to Process Discovery
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered…
Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modeling
Humam Kourani, Anton Antonov, Alessandro Berti +1
The utility of Large Language Models (LLMs) in analytical tasks is rooted in their vast pre-trained knowledge, which allows them to interpret ambiguous inputs and infer missing inf…
Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithm…
Translating Workflow Nets into the Partially Ordered Workflow Language
Humam Kourani, Gyunam Park, Wil van der Aalst
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. However, its adoptio…
Evaluating Large Language Models on Business Process Modeling: Framework, Benchmark, and Self-Improvement Analysis
Humam Kourani, Alessandro Berti, Daniel Schuster +1
Large Language Models (LLMs) are rapidly transforming various fields, and their potential in Business Process Management (BPM) is substantial. This paper assesses the capabilities…
Leveraging Large Language Models for Enhanced Process Model Comprehension
Humam Kourani, Alessandro Berti, Jasmin Hennrich +6
In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become…
Bridging Domain Knowledge and Process Discovery Using Large Language Models
Ali Norouzifar, Humam Kourani, Marcus Dees +1
Discovering good process models is essential for different process analysis tasks such as conformance checking and process improvements. Automated process discovery methods often o…
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…
ProMoAI: Process Modeling with Generative AI
Humam Kourani, Alessandro Berti, Daniel Schuster +1
ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, e…
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…
Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, and Evaluation Strategies
Alessandro Berti, Humam Kourani, Hannes Hafke +2
Using Large Language Models (LLMs) for Process Mining (PM) tasks is becoming increasingly essential, and initial approaches yield promising results. However, little attention has b…