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Stream Reasoning in Temporal Datalog

Alessandro Ronca‚ Mark Kaminski‚ Bernardo Cuenca Grau‚ Boris Motik and Ian Horrocks


Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.

Book Title
Proceedings of the Thirty−Second AAAI Conference on Artificial Intelligence‚ (AAAI−18)‚ New Orleans‚ Louisiana‚ USA‚ February 2–7‚ 2018