49 citations · 51 across the 12 of their papers we have counts for
13 papers
ERP Event Logs: A Canonical Form for KPI Time-Series Extraction and Characterization
Sherri Hadian, Adrian Rebmann, Atacan Korkmaz +2
Enterprise resource planning (ERP) systems record operations as transactions spread across hundreds of normalized relational tables. Extracting an operational key performance indic…
Organizational Memory for Agentic Business Process Execution
Lukas Kirchdorfer, Adrian Rebmann, Christian Warmuth +3
LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLMs lack the organization-spec…
Agent Behavior Mining: Generative AI Agent Governance in Business Processes
Hoang Vu, Maximilian Körner, Adrian Rebmann +4
As organizations increasingly deploy generative AI agents to automate business processes, they face a governance dilemma: although these agents can increase operational flexibility…
Bridging Imperative Process Models and Process Data Queries-Translation and Relaxation
Abdur Rehman Anwar Qureshi, Adrian Rebmann, Timotheus Kampik +2
Business process management is increasingly practiced using data-driven approaches. Still, classical imperative process models, which are typically formalized using Petri nets, are…
LLMs that Understand Processes: Instruction-tuning for Semantics-Aware Process Mining
Vira Pyrih, Adrian Rebmann, Han van der Aa
Process mining is increasingly using textual information associated with events to tackle tasks such as anomaly detection and process discovery. Such semantics-aware process mining…
Detecting Undesired Process Behavior by Means of Retrieval Augmented Generation
Michael Grohs, Adrian Rebmann, Jana-Rebecca Rehse
Conformance checking techniques detect undesired process behavior by comparing process executions that are recorded in event logs to desired behavior that is captured in a dedicate…
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks
Adrian Rebmann, Fabian David Schmidt, Goran Glavaš +1
Large language models (LLMs) have shown to be valuable tools for tackling process mining tasks. Existing studies report on their capability to support various data-driven process a…
Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks
Adrian Rebmann, Fabian David Schmidt, Goran Glavaš +1
The process mining community has recently recognized the potential of large language models (LLMs) for tackling various process mining tasks. Initial studies report the capability…
Mining Constraints from Reference Process Models for Detecting Best-Practice Violations in Event Logs
Adrian Rebmann, Timotheus Kampik, Carl Corea +1
Detecting undesired process behavior is one of the main tasks of process mining and various conformance-checking techniques have been developed to this end. These techniques typica…
From OCEL to DOCEL -- Datasets and Automated Transformation
Alexandre Goossens, Adrian Rebmann, Johannes De Smedt +2
Object-centric event data represent processes from the point of view of all the involved object types. This perspective has gained interest in recent years as it supports the analy…
Large Process Models: A Vision for Business Process Management in the Age of Generative AI
Timotheus Kampik, Christian Warmuth, Adrian Rebmann +12
The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over…
GECCO: Constraint-driven Abstraction of Low-level Event Logs
Adrian Rebmann, Matthias Weidlich, Han van der Aa
Process mining enables the analysis of complex systems using event data recorded during the execution of processes. Specifically, models of these processes can be discovered from e…
Extracting Semantic Process Information from the Natural Language in Event Logs
Adrian Rebmann, Han van der Aa
Process mining focuses on the analysis of recorded event data in order to gain insights about the true execution of business processes. While foundational process mining techniques…