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A graph-based database partitioning method for parallel olap query processing

Title
A graph-based database partitioning method for parallel olap query processing
Authors
Nam, Yoon-MinKim, Min-SooHan, Donghyoung
DGIST Authors
Kim, Min-Soo
Issue Date
2018-04-19
Citation
34th IEEE International Conference on Data Engineering, ICDE 2018, 1037-1048
Type
Conference
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. ? 2018 IEEE.
URI
http://hdl.handle.net/20.500.11750/9501
DOI
10.1109/ICDE.2018.00096
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Kim, Min-Soo InfoLab
  • Research Interests Big Data Systems; Big Data Mining & Machine Learning; Big Data Bioinformatics; 데이터 마이닝 및 빅데이터 분석; 바이오인포메틱스 및 뉴로인포메틱스; 뇌-기계 인터페이스(BMI)
Files:
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Collection:
Department of Information and Communication EngineeringInfoLab2. Conference Papers


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