arXiv:2605.02488v2 Announce Type: replace 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 typically studied in isolation, complicating their comparison in terms of expressivity and obscuring
The continuous evolution of AI and real-time data processing necessitates more efficient and robust methods for identifying complex patterns in high-velocity data streams.
Improving the efficiency of temporal Datalog materialisation directly enhances the capability of AI systems to perform real-time composite event recognition, crucial for critical applications.
This research provides a more sophisticated framework for defining and evaluating complex event patterns, potentially leading to more reliable and faster detection of critical situations across various applications.
- · AI/ML Research
- · Stream Processing Companies
- · Cybersecurity Sector
- · Financial Fraud Detection
- · Inefficient Legacy Event Recognition Systems
- · Systems Reliant on Manual Pattern Definition
More accurate and faster real-time anomaly detection in enterprise and security systems.
Reduced latency in responding to emerging threats and opportunities identified through event correlation.
The proliferation of context-aware, adaptive AI agents capable of proactive decision-making based on complex temporal logic.
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Read at arXiv cs.AI