Towards A Cache-Enabled, Order-Aware, Ontology-Based Stream Reasoning Framework

While streaming data have become increasingly more popular in business and research communities, semantic models and processing software for streaming data have not kept pace. Traditional semantic solutions have not addressed transient data streams. Semantic web languages (e.g., RDF, OWL) have typically addressed static data settings and linked data approaches have predominantly addressed static or growing data repositories. Streaming data settings have some fundamental differences; in particular, data are consumed on the fly and data may expire.

Stream reasoning, a combination of stream processing and semantic reasoning, has emerged with the vision of providing ``smart`` processing of streaming data. C-SPARQL is a prominent stream reasoning system that handles semantic (RDF) data streams. Many stream reasoning systems including C-SPARQL use a sliding window and use data arrival time to evict data. For data streams that include expiration times, a simple arrival time scheme is inadequate if the window size does not match the expiration period.

In this paper, we propose a cache-enabled, order-aware, ontology-based stream reasoning framework. This framework consumes RDF streams with expiration timestamps assigned by the streaming source. Our framework utilizes both arrival and expiration timestamps in its cache eviction policies. In addition, we introduce the notion of ``semantic importance`` which aims to address the relevance of data to the expected reasoning, thus enabling the eviction algorithms to be more context- and reasoning-aware when choosing what data to maintain for question answering. We evaluate this framework by implementing three different prototypes and utilizing five metrics. The trade-offs of deploying the proposed framework are also discussed.


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Stream reasoning systems are typically built using a fixed-size data windows, into which new elements from the stream are continuously added, and from which other elements are dropped. Our project develops a new class of window management algorithms based on semantic importance, using RDF data semantics to rank the elements of the window for their importance. Using semantics achieves higher reasoning performance, particularly for smaller window sizes, and takes account of background knowledge in the domain.

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.

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