Communications of the ACM
Database Partitioning Method Supports High-Performance Data Processing
Daegu Gyeongbuk Institute of Science and Technology researchers have developed a core technology that supports fast, efficient large-scale data analysis.
Credit: Techniexpert.com
Researchers at Daegu Gyeongbuk Institute of Science and Technology (DGIST) in South Korea have developed a core technology that supports fast, efficient large-scale data analysis.
The researchers developed data management and processing techniques for a relational database called Graph-based Partitioning Table (GPT) technology, which demonstrated more than four times faster query performance on average compared to Spark SQL.
The new GPT technology supports an efficient database partitioning method for relational databases, which can eliminate expensive network communication among machines during query processing, resolving critical issues in database partitioning methods and parallel and distributed query processing technologies.
Said DGIST researcher Min-Soo Kim, "We expect that the technology for processing relational data we developed from this research will be very useful in the future as data becomes larger and complex."
From Daegu Gyeongbuk Institute of Science and Technology
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA
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