Abstract
To achieve efficient large-scale RDF data queries, we designed a parallel two-phase query strategy-PAQS for large-scale RDF data based on MapReduce, which is divided into two stages: the SPARQL pretreatment stage and the distributed query execution stage. In the SPARQL pretreatment stage, a SPARQL query classification algorithm is implemented, which determines the join order of connection variables by calculating the correlation between the variables in a SPARQL query statement; then, the join between SPARQL clauses is divided into the minimum number of MapReduce jobs according to the connection variables. The distributed query execution phase accomplishes large-scale RDF data query concurrently based on MapReduce jobs from the SPARQL pretreatment stage. The experimental results on the LUMB benchmark set indicate that PAQS can query large-scale RDF data with good efficiency, stability, and scalability.
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The National Natural Science Foundation of China under Grant No. 61301136, No. 61272148 and No. 61602525.
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Yang, L., Yang, L., Niu, J., Hu, Z., Long, J., Zheng, M. (2016). A Semantic Data Parallel Query Method Based on Hadoop. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_29
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DOI: https://doi.org/10.1007/978-3-319-48740-3_29
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