Summary of Conformance Checking Of Fuzzy Logs Against Declarative Temporal Specifications, by Ivan Donadello et al.
Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications
by Ivan Donadello, Paolo Felli, Craig Innes, Fabrizio Maria Maggi, Marco Montali
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to conformance checking, which is crucial for event-driven process execution monitoring. The traditional assumption that event data accurately represents actual processes has been questioned due to indirect event recognition pipelines and uncertainty. In this context, the authors introduce a fuzzy semantics to address uncertainty in activity executions. They define a fuzzy counterpart of Linear Temporal Logic over Finite Traces (LTLf) and cast conformance checking as a verification problem within this logic. A proof-of-concept implementation using PyTorch allows for efficient multiple-trace conformance checks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to check if events match what we think should happen in a process. Usually, we assume that event data shows exactly what’s happening in the process. But sometimes, events aren’t directly recorded and are instead figured out from other events. This can make it unclear which activities are actually happening. The authors suggest a new way of looking at this uncertainty by treating activities as fuzzy (not black or white). They create a special logic to work with these fuzzy events and show how to use it to check if events match what we want. This is useful for monitoring processes that involve many events. |
Keywords
» Artificial intelligence » Semantics
