Efficient window aggregate method on array database system

Li Jiang, Hideyuki Kawashima, Osamu Tatebe

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

An array database is effective for managing a massive amount of sensor data, and the window aggregate is a popular operator. We propose an efficient window aggregate method over multi-dimensional array data based on incremental computation. We improve five types of aggregates by exploiting different data structures: list for summation and average, heap for maximum and minimum, and balanced binary search tree for percentile. We design and fully implement the proposed method in SciDB using the plugin mechanism. In addition, we evaluate the performance through experiments using the synthetic and JRA-55 meteorological datasets. The results of our experiments on SciDB are consistent with our analytic findings. The proposed method achieves a 17.9x, 12.5x, and 10.2x performance improvement for minimum, summation, and percentile operators, respectively, compared with SciDB built-in operators. These results align with our time-complexity analysis results.

Original languageEnglish
Pages (from-to)867-877
Number of pages11
JournalJournal of information processing
Volume24
Issue number6
DOIs
Publication statusPublished - 2016
Externally publishedYes

Keywords

  • Array database
  • Incremental computation
  • Multi-dimensional array
  • Window aggregate

ASJC Scopus subject areas

  • Computer Science(all)

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