Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams

• URL: http://arxiv.org/abs/2505.05880v1
• Date: Fri, 09 May 2025 08:45:07 GMT
• Title: Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams
• Authors: Bettina Fazzinga, Sergio Flesca, Filippo Furfaro, Luigi Pontieri, Francesco Scala,
• Abstract summary: We propose a data/computation-efficient neuro-symbolic approach to the interpretation problem.<n>Considering the urgent need of developing Green AI solutions, we propose a data/computation-efficient neuro-symbolic approach to the problem.
• Score: 18.821902752237204
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Abstract Argumentation Framework (AAF), one can elegantly analyze plausible event interpretations (possibly in an aggregated form), as well as offer explanations for those that conflict with prior process knowledge. Since, in settings where event-to-activity mapping is highly uncertain (or simply under-specified) this reasoning-based approach may yield lowly-informative results and heavy computation, one can think of discovering a sequencetagging model, trained to suggest highly-probable candidate event interpretations in a context-aware way. However, training such a model optimally may require using a large amount of manually-annotated example traces. Considering the urgent need of developing Green AI solutions enabling environmental and societal sustainability (with reduced labor/computational costs and carbon footprint), we propose a data/computation-efficient neuro-symbolic approach to the problem, where the candidate interpretations returned by the example-driven sequence tagger is refined by the AAF-based reasoner. This allows us to also leverage prior knowledge to compensate for the scarcity of example data, as confirmed by experimental results; clearly, this property is particularly useful in settings where data annotation and model optimization costs are subject to stringent constraints.

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