Syed Gillani
ABSTRACT
This paper proposes a new approach for the the incremental evaluation of RDF graph streams over sliding windows. Our system, called "SPECTRA", combines a novel formof RDF graph summarisation, a new incremental evaluation method and adaptive indexing techniques. We materialise the summarised graph from each event using vertically partitioned views to facilitate the fast hash-joins for all types of queries. Our incremental and adaptive indexing is a byproduct of query processing, and thus provides considerable advantages over offline and online indexing. Furthermore, contrary to the existing approaches, we employ incremental evaluation of triples within a window. This results in considerable reduction in response time, while cutting the unnecessary cost imposed by recomputation models for each triple insertion and eviction within a defined window. We show that our resulting system is able to cope with complex queries and datasets with clear benefits. Our experimental results on both synthetic and real-world datasets show up to an order of magnitude of performance improvements as compared to state-of-the-art systems.
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- SPECTRA: Continuous Query Processing for RDF Graph Streams Over Sliding Windows
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