Efficient Temporal Datalog Materialisation for Composite Event Recognition
📰 ArXiv cs.AI
Learn how to efficiently detect composite events in real-time streams using Temporal Datalog Materialisation
Action Steps
- Define composite events using temporal patterns over simpler events
- Implement Temporal Datalog Materialisation to evaluate these patterns
- Optimise the materialisation process for efficient event detection
- Apply this technique to high-velocity streams of symbolic events
- Evaluate the performance of the system using metrics such as latency and throughput
Who Needs to Know This
Data scientists and software engineers working on event-driven systems can benefit from this technique to improve the performance of their applications
Key Insight
💡 Temporal Datalog Materialisation can efficiently detect composite events in real-time streams by evaluating temporal patterns over simpler events
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Efficiently detect composite events in real-time streams using Temporal Datalog Materialisation #EventRecognition #StreamReasoning
Key Takeaways
Learn how to efficiently detect composite events in real-time streams using Temporal Datalog Materialisation
Full Article
Title: Efficient Temporal Datalog Materialisation for Composite Event Recognition
Abstract:
arXiv:2605.02488v1 Announce Type: new Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typi
Abstract:
arXiv:2605.02488v1 Announce Type: new Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events. This demand has motivated the development of (i) event specification languages, which define composite events via temporal patterns over simpler events, and (ii) stream reasoning frameworks, evaluating patterns expressed in these languages. However, event specification languages are typi
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