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Algebraic Optimization for Processing Graph Pattern Queries in the Cloud
March-April 2013 (vol. 17 no. 2)
pp. 52-61
Kemafor Anyanwu, North Carolina State University
HyeongSik Kim, North Carolina State University
Padmashree Ravindra, North Carolina State University
MapReduce platforms such as Hadoop are now the de facto standard for large-scale data processing, but they have significant limitations for join-intensive workloads typical in Semantic Web processing. This article overviews an algebraic optimization approach based on a Nested TripleGroup Data Model and Algebra (NTGA) that minimizes overall processing costs by reducing the number of MapReduce cycles. It also presents an approach for integrating NTGA-based processing of graph pattern queries into Apache Pig and compares it to execution plans using relational-style algebra operators.
Index Terms:
Resource description framework,Optimization,Query processing,Data processing,query processing,query languages,database management,information technology and systems
Citation:
Kemafor Anyanwu, HyeongSik Kim, Padmashree Ravindra, "Algebraic Optimization for Processing Graph Pattern Queries in the Cloud," IEEE Internet Computing, vol. 17, no. 2, pp. 52-61, March-April 2013, doi:10.1109/MIC.2012.22
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