Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams
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arXiv:2505.05880v2 Announce Type: replace Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between trace events and reference business activities, this entails an interpretation problem, amounting to translating each event of any ongoing trace into the corresponding step of the activity instance. Building on a recent approach that frames the interpretation problem as an acceptance problem within an Abst
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Title: Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams
Abstract:
arXiv:2505.05880v2 Announce Type: replace Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between trace events and reference business activities, this entails an interpretation problem, amounting to translating each event of any ongoing trace into the corresponding step of the activity instance. Building on a recent approach that frames the interpretation problem as an acceptance problem within an Abst
Abstract:
arXiv:2505.05880v2 Announce Type: replace Abstract: Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between trace events and reference business activities, this entails an interpretation problem, amounting to translating each event of any ongoing trace into the corresponding step of the activity instance. Building on a recent approach that frames the interpretation problem as an acceptance problem within an Abst
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