Incremental window aggregates over array database

Li Jiang, Hideyuki Kawashima, Osamu Tatebe

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

We propose an efficient window aggregation method over multi-dimensional array data based on incremental computation. We improve several aggregations with different data structures exploited to achieve efficient computation: list for sum and avg, heap for max and min, and balanced binary search tree for percentile. We present time complexity analysis for the methods, and then evaluate performance with experiments in SciDB array database system with both synthetic and JRA55 meteorological dataset. Our analysis shows that performance improvement is proportional to the window size in the last dimension in theory, and the result of experiment is consistent with the analysis. In certain cases, it shows an acceleration factor more than 13 by the proposed method with percentile, while a factor over 28 with maximum.

本文言語English
ホスト出版物のタイトルProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
編集者Wo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
出版社Institute of Electrical and Electronics Engineers Inc.
ページ183-188
ページ数6
ISBN(電子版)9781479956654
DOI
出版ステータスPublished - 2015 1 7
外部発表はい
イベント2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
継続期間: 2014 10 272014 10 30

出版物シリーズ

名前Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
国/地域United States
CityWashington
Period14/10/2714/10/30

ASJC Scopus subject areas

  • 人工知能
  • 情報システム

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