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ホウチン テルヒサ
Teruhisa Hochin
寶珍 輝尚 所属 追手門学院大学 理工学部 情報工学科 職種 教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2015/08/03 |
| 形態種別 | 論文 |
| 査読 | 査読あり |
| 標題 | Fields for efficient analysis of big data |
| 執筆形態 | 共著・編著（代表編著を除く） |
| 掲載誌名 | 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015 - Proceedings |
| 掲載区分 | 国外 |
| 出版社・発行元 | IEEE Computer Society |
| 巻・号・頁 | pp.515-520 |
| 担当区分 | 筆頭著者,責任著者 |
| 著者・共著者 | Teruhisa Hochin,Hiroki Nomiya |
| 概要 | This paper introduces the concept of fields for the purpose of increasing the efficiency of the analysis of big data. We focus specifically on time series data. Data are treated as points in a space. A field is a named subspace within that space. A field may restrict the position of a point. The subspace of a field may change according to the points included in the field. It may also be nested. After formally defining the concept of a field, we describe an approach to processing big data that incorporates this notion. By assigning a field to a meaningful portion, we can treat only the portions that we are interested in. As this reduces the amount of data processed, it results in the efficient processing of big data. |
| DOI | 10.1109/SNPD.2015.7176251 |
| DBLP ID | conf/snpd/HochinN15 |
| PermalinkURL | https://dblp.uni-trier.de/rec/conf/snpd/2015 |
| researchmap用URL | https://dblp.uni-trier.de/db/conf/snpd/snpd2015.html#HochinN15 |