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Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
ISBN: 978-1-4673-2892-0
pp: 139-146
Recently, the number and size of RDF data collections has increased rapidly making the issue of scalable processing techniques crucial. The MapReduce model has become a de facto standard for large scale data processing using a cluster of machines in the cloud. Generally, RDF query processing creates join-intensive workloads, resulting in lengthy MapReduce workflows with expensive I/O, data transfer, and sorting costs. However, the MapReduce computation model provides limited static optimization techniques used in relational databases (e.g., indexing and cost-based optimization). Consequently, dynamic optimization techniques for such join-intensive tasks on MapReduce need to be investigated. In some previous efforts, we propose a Nested Triple Group data model and Algebra (NTGA) for efficient graph pattern query processing in the cloud. Here, we extend this work with a scan-sharing technique that is used to optimize the processing of graph patterns with repeated properties. Specifically, our scan-sharing technique eliminates the need for repeated scanning of input relations when properties are used repeatedly in graph patterns. A formal foundation underlying this scan sharing technique is discussed as well as an implementation strategy that has been integrated in the Apache Pig framework is presented. We also present a comprehensive evaluation demonstrating performance benefits of our NTGA plus scan-sharing approach.
Resource description framework, Data models, Cloning, Algebra, Optimization, Pattern matching, Context, Optimization Techniques, cloud computing, MapReduce, SPARQL, RDF Graph Pattern Matching
HyeongSik Kim, Padmashree Ravindra, Kemafor Anyanwu, "Scan-Sharing for Optimizing RDF Graph Pattern Matching on MapReduce", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 139-146, doi:10.1109/CLOUD.2012.14
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