75 citations · 91 across the 10 of their papers we have counts for
11 papers
Machine learning for journal entry testing: A type-aware evaluation of anomaly detectors under a review budget
Jan Gronewald, Michel Scherer, Nijat Mehdiyev
Journal entry anomaly detectors are commonly evaluated on the full population with ROC-AUC, precision and recall, ignoring the review budget and which anomaly types are found. We p…
Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
Jan Gronewald, Andreas Emrich, Nijat Mehdiyev
Pre-demolition assessment, the regulated audit process at the heart of urban mining, is an information process in which AI support must serve qualified auditors who remain accounta…
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
Alexander Rombach, Chantale Lauer, Nijat Mehdiyev
Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns prese…
Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda
Alexander Rombach, Chantale Lauer, Nijat Mehdiyev
LLM-based agents are entering regulated industries where they automate judgment intensive quality management processes. We argue that symbolic structures already embedded in these…
Human-Centered Evaluation of an LLM-Based Process Modeling Copilot: A Mixed-Methods Study with Domain Experts
Chantale Lauer, Peter Pfeiffer, Nijat Mehdiyev
Integrating Large Language Models (LLMs) into business process management tools promises to democratize Business Process Model and Notation (BPMN) modeling for non-experts. While a…
Assessing the Business Process Modeling Competences of Large Language Models
Chantale Lauer, Peter Pfeiffer, Alexander Rombach +1
The creation of Business Process Model and Notation (BPMN) models is a complex and time-consuming task requiring both domain knowledge and proficiency in modeling conventions. Rece…
Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining,…
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-maki…
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
As data-driven intelligent systems advance, the need for reliable and transparent decision-making mechanisms has become increasingly important. Therefore, it is essential to integr…
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
Nijat Mehdiyev, Peter Fettke
The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine…
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
Nijat Mehdiyev, Peter Fettke
This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining pro…