Fast window aggregate on array database by recursive incremental computation

Li Jiang, Hideyuki Kawashima, Osamu Tatebe

研究成果: Conference contribution

抄録

An array database is effective for managing and analyzing multidimensional scientific big data, and the window aggregate is an important operator in array databases. This paper proposes a method that exploits the scheme of incremental computation to accelerate the execution of window aggregates considerably. Six types of aggregate are improved using different designs of buffer tools to eliminate redundant computation. Our proposed recursive incremental computation method completely eliminates all redundant computation and achieves an improvement factor of the total window size compared with the naive method. This proposed method is fully implemented in SciDB. It improved performance by a factor of 10 on an earth science benchmark and by a factor of 64 on synthetic workloads with a certain data setting when compared with SciDB's built-in window operator.

本文言語English
ホスト出版物のタイトルProceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ101-110
ページ数10
ISBN(電子版)9781509042722
DOI
出版ステータスPublished - 2017 3 3
外部発表はい
イベント12th IEEE International Conference on e-Science, e-Science 2016 - Baltimore, United States
継続期間: 2016 10 232016 10 27

Other

Other12th IEEE International Conference on e-Science, e-Science 2016
国/地域United States
CityBaltimore
Period16/10/2316/10/27

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 環境科学(その他)
  • 医学(その他)
  • 社会科学(その他)
  • 農業および生物科学(その他)
  • コンピュータ サイエンスの応用

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