Scalable Multi-Query Optimization for SPARQL

Provided by: The University of Tulsa
Topic: Big Data
Format: PDF
In this paper, the authors revisit the classical problem of multi-query optimization in the context of RDF/SPARQL. The authors show that the techniques developed for relational and semi-structured data/query languages are hard, if not impossible, to be extended to account for RDF data model and graph query patterns expressed in SPARQL. In light of the NP-hardness of the multi-query optimization for SPARQL, they propose heuristic algorithms that partition the input batch of queries into groups such that each group of queries can be optimized together. An essential component of the optimization incorporates an efficient algorithm to discover the common sub-structures of multiple SPARQL queries and an effective cost model to compare candidate execution plans.

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