Towards Smart Cache Management for Ontology Based, History-Aware Stream Reasoning

Stream reasoning is an exciting multidisciplinary research area that combines stream processing and semantic reasoning. Its goal is to not only process a dynamic data stream, but also to extract explicit and implicit information on-the-fly. One of its challenges is managing history awareness: how much and which historical data should be held and for how long as we continuously query and reason on an ever changing stream of linked data? In this paper, we propose an innovative approach to enable history-aware reasoning by utilizing semantic technologies in a data cache with a statistics-based cache management policy.

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AIM will advance streaming reasoning techniques to overcome a limitation in contemporary inference that performs analysis only over data in a fixed cache or a moving window. This research will lead to methods that continuously shed light on proposed hypotheses as new knowledge arrives from streams of propositions, with a particular emphasis on the effect that removing the expectation of completeness has on the soundness and performance of symbolic deduction platforms. The work will address challenges in sampling rates, cache maintenance, deductive reasoning, and ranking of conclusions.