Titlebook: Cooperative Information Systems; 29th International C Mohamed Sellami,Maria-Esther Vidal,Hervé Panetto Conference proceedings 2024 The Edit

ENACT

Beyond Rule-Based Named Entity Recognition and?Relation Extraction for?Process Model Generation frompipeline, which not only eliminates the substantial overhead associated with feature engineering and rule definition, but also enables adaptation to different datasets, entity and relation types, and new domains. Additionally, the largest available dataset (PET) for the first subtask, contains no in

INCUR

On the?Semantic Transparency of?Declarative Process Models: The Case of?Constraintsdy concentrates on Dynamic Condition Response (DCR) Graphs, a representative notation of DPMs extensively used in industry and academia. The research employs semi-structured interviews with experts in DCR Graphs, as well as an analysis of semantic transparency based on theoretical models of understa

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Benign

Discovery of?Workflow Patterns - A Comparison of?Process Discovery Algorithmse results shows that the discovery algorithms mainly cover basic control flow patterns and iterative structures, while multi-instance, state-base, and cancellation patterns are only partially covered.

丑惡

GAVEL

,The Economics of Manufacturers’ Rebates, autonomously. This work presents AMAS4DC a generic Dynamic Clustering model based on Adaptive Multi-Agent System approach. AMAS4DC processes acquired data on the fly using local similarity evaluation for cluster’s creation or fusion. AMAS4DC is then instantiated on two use cases: the dynamic cluste

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流出

,Why So Many Prices End with “9”,heir right to take part in the system operations with certificates and to secure their communications with asymmetric cryptography. As a result, agents can communicate securely without the risk of their identities being stolen and their communications being tampered with. They are also being able to

VICT

Values, Ideology, and Value Orientations,mpliance “red flags” that we seeded in a use case. Using a qualitative approach to analyze the data, all the groups discussed the majority of the GDPR principles and identified more than 80% of the seeded red flags, with privacy experts identifying the most. This research provides preliminary result

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