This work demonstrates use of Materialized Views to enhance query performance for partitioned RDF data. Given a query, our system determines which views or combinations thereof can be used to answer it. Break- even analysis for the proposed system has been done based on view materialization and refreshment costs. The system performance was evaluated for 7 query types, 3 having Sub-Obj joins. It shows that our approach reduces query response time by an average of 26% for all query types w.r.t response time using just vertical partitioning. Specifically, for queries with Sub-Obj joins, the average reduction is by 37%. On scaling data up 8 times, the reduction changed from 37% to 79% for queries with Sub-Obj joins, and from 26% to 51% on an average for all query types. With the proposed technique, Semantic Web Applications shall be more interactive since queries having Sub-Obj. joins are expected for them. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Vasani, S., Pandey, M., Bhise, M., & Padiya, T. (2013). Faster query execution for partitioned RDF data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7753 LNCS, pp. 547–560). Springer Verlag. https://doi.org/10.1007/978-3-642-36071-8_44
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