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- W3138799689 abstract "As the scale of RDF data increases gradually, the requirements for efficient processing technologies on RDF data in the distributed environment has become a hot topic. Most existing methods have two main issues. First, massive storage redundancy often occurs to materialize semi-join results, but only a part is frequently accessed in most queries. Second, the queries are allocated to the whole cluster, causing a considerable duplication on intermediate results and hence increasing the communication cost. In this paper, we propose a new approach named FP-ExtVP, using a hybrid partitioning schema to reduce the storage requirement without damaging the query performance. In addition, we create an index named P-tree to allocate the queries only to the mentioned nodes, instead of the whole cluster, in order to reduce the shuffle cost. The experiments on several benchmarks demonstrate that, comparing to the native S2RDF, our method outperforms on Star, Snowflake and Complex queries, by reducing nearly a half of the storage requirements." @default.
- W3138799689 created "2021-03-29" @default.
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- W3138799689 date "2020-12-10" @default.
- W3138799689 modified "2023-09-25" @default.
- W3138799689 title "FP-ExtVP: Accelerating Distributed SPARQL queries by Exploiting Load-adaptive Partitioning" @default.
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- W3138799689 doi "https://doi.org/10.1109/bigdata50022.2020.9378227" @default.
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