Efficient Window Aggregate Method on Array Database System

• Item type: Journal(1)
• 公開日: 2016-10-15
• タイトル: Efficient Window Aggregate Method on Array Database System
• タイトル / 言語: en
• タイトル: Efficient Window Aggregate Method on Array Database System
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：ユビキタスコンピューティングシステム（Ⅴ）] multi-dimensional array, incremental computation, array database, window aggregate
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Systems and Information Engineering, University of Tsukuba／Presently with Google Dublin
• 著者所属: Center for Computational Sciences, University of Tsukuba
• 著者所属: Center for Computational Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Graduate School of Systems and Information Engineering, University of Tsukuba / Presently with Google Dublin
• 著者所属(英): en
• 著者所属(英): Center for Computational Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Center for Computational Sciences, University of Tsukuba
• 著者名: Li, Jiang; Hideyuki, Kawashima; Osamu, Tatebe
• 著者名(英): Li, Jiang; Hideyuki, Kawashima; Osamu, Tatebe
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.6 (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.24(2016) No.6 (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 57, 号 10, 発行日 2016-10-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764