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• W2963947619 abstract "One of the major challenges in large-scale data processing with MapReduce is the smart computation of joins. Since Semantic Web datasets published in RDF have increased rapidly over the last few years,scalable join techniques become an important issue for SPARQL query processing as well. In this paper, we introduce the Map-Side Index Nested Loop Join (MAPSIN join) which combines scalable indexing capabilities of NoSQL data stores like HBase, that suffer from an insufficient distributedprocessing layer, with MapReduce, which in turn does not provide appropriate storage structures for efficient large-scale join processing. While retaining the flexibility of commonly used reduce-side joins,we leverage the effectiveness of map-side joins without any changes to the underlying framework. We demonstrate the significant benefits of MAPSIN joins for the processing of SPARQL basic graph patterns on large RDF datasets by an evaluation with the LUBM and SP2Bench benchmarks. For selective queries, MAPSIN join based query execution outperforms reduce-side join based execution by an order of magnitude." @default.
• W2963947619 created "2019-07-30" @default.
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• W2963947619 date "2012-01-01" @default.
• W2963947619 modified "2023-09-23" @default.
• W2963947619 title "Cascading Map-Side joins over HBase for scalable join processing" @default.
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• W2963947619 doi "https://doi.org/10.6094/unifr/12269" @default.
• W2963947619 hasPublicationYear "2012" @default.
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