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- W2794696543 abstract "As the amount of data to process increases, a scalable and efficient horizontal database partitioning method becomes more important for OLAP query processing in parallel database platforms. Existing partitioning methods have a few major drawbacks such as a large amount of data redundancy and not supporting join processing without shuffle in many cases despite their large data redundancy. We elucidate the drawbacks arise from their tree-based partitioning schemes and propose a novel graph-based database partitioning method called GPT that improves query performance with lower data redundancy. Through extensive experiments using three benchmarks, we show that GPT significantly outperforms the state-of-the-art method in terms of both storage overhead and query performance." @default.
- W2794696543 created "2018-04-06" @default.
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- W2794696543 date "2018-04-01" @default.
- W2794696543 modified "2023-10-18" @default.
- W2794696543 title "A Graph-Based Database Partitioning Method for Parallel OLAP Query Processing" @default.
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- W2794696543 doi "https://doi.org/10.1109/icde.2018.00096" @default.
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