The Window Validity Problem in Rule-Based Stream Reasoning
This work addresses the need for near real-time, resource-efficient stream processing in applications like data analysis, though it appears incremental as it builds on existing rule-based temporal query languages.
The paper tackles the problem of efficient stream reasoning by proposing a recursive fragment of temporal Datalog with tractable data complexity and studying a generic algorithm to minimize memory usage through the window validity problem.
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.