The paper identifies and formally defines assumptions for unambiguous representation and manipulation of dynamic relationships in object-centric event logs, validated on existing logs.
Towards a simple and extensible standard for object-centric event data (oced)–core model, design space, and lessons learned
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Agent Behavior Mining to translate generative AI agent activities into standardized process logs, enabling process mining for policy deviation detection and variability quantification in business processes, with practitioner feedback.
Four granularity-adjustment operations for object-centric event logs are formally defined, implemented in Python, and applied to a university course log, with claims of improved model fitness and precision.
citing papers explorer
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Detecting Dynamic Relationships in Object-Centric Event Logs
The paper identifies and formally defines assumptions for unambiguous representation and manipulation of dynamic relationships in object-centric event logs, validated on existing logs.
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Agent Behavior Mining: Generative AI Agent Governance in Business Processes
Introduces Agent Behavior Mining to translate generative AI agent activities into standardized process logs, enabling process mining for policy deviation detection and variability quantification in business processes, with practitioner feedback.
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Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations
Four granularity-adjustment operations for object-centric event logs are formally defined, implemented in Python, and applied to a university course log, with claims of improved model fitness and precision.