Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917278451236864 |
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| author | Cecconi, Alessio Barbaro, Luca Di Ciccio, Claudio Senderovich, Arik |
| author_facet | Cecconi, Alessio Barbaro, Luca Di Ciccio, Claudio Senderovich, Arik |
| contents | Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability in discovery, checking, and drift detection contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_05418 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach Cecconi, Alessio Barbaro, Luca Di Ciccio, Claudio Senderovich, Arik Artificial Intelligence Logic in Computer Science Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability in discovery, checking, and drift detection contexts. |
| title | Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2305.05418 |