Towards expressive rule induction on IP network event streams

Wrench, C., Stahl, F. ORCID: https://orcid.org/0000-0002-4860-0203, Di Fatta, G., Karthikeyan, V. and Nauck, D. (2015) Towards expressive rule induction on IP network event streams. In: AI-2015 Thirty-fifth SGAI International Conference on Artificial Intelligence, 15-17 December 2015, Cambridge.

Abstract/Summary

In order to gain insights into events and issues that may cause errors and outages in parts of IP networks, intelligent methods that capture and express causal relationships online (in real-time) are needed. Whereas generalised rule induction has been explored for non-streaming data applications, its application and adaptation on streaming data is mostly undeveloped or based on periodic and ad-hoc training with batch algorithms. Some association rule mining approaches for streaming data do exist, however, they can only express binary causal relationships. This paper presents the ongoing work on Online Generalised Rule Induction (OGRI) in order to create expressive and adaptive rule sets real-time that can be applied to a broad range of applications, including network telemetry data streams.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/56395
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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