RkNN Query on Road Network Distances

• Item type: Journal(1)
• 公開日: 2015-03-15
• タイトル: RkNN Query on Road Network Distances
• タイトル / 言語: en
• タイトル: RkNN Query on Road Network Distances
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：学生・若手研究者論文] location based services, RkNN, road network, distance materialization
• 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 Science and Engineering, Saitama University
• 著者所属: Graduate School of Science and Engineering, Saitama University
• 著者所属: Graduate School of Science and Engineering, Saitama University
• 著者所属: Graduate School of Science and Engineering, Saitama University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Saitama University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Saitama University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Saitama University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Saitama University
• 著者名: AyeThidaHlaing; TinNilarWin; Htoo, Htoo; Yutaka, Ohsawa
• 著者名(英): Aye, ThidaHlaing; Tin, NilarWin; Htoo, Htoo; Yutaka, Ohsawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Reverse k-nearest neighbor (RkNN) queries on road network distances require long processing times because most conventional algorithms require a k-nearest neighbor (kNN) search on every visited node. This causes a large number of node expansions; therefore, the processing time is drastically increased when data points are sparsely distributed. In this paper, we propose a fast RkNN search algorithm that runs using a simple materialized path view (SMPV). In addition, we adopt an incremental Euclidean restriction strategy for fast kNN queries, the main function in RkNN queries. The SMPV used in our proposed algorithm only constructs an individual partitioned subgraph; therefore, the amount of data is drastically reduced compared to conventional materialized path views (MPVs). According to our experimental results using real road network data, our proposed method achieved a processing time that was 100 times faster than conventional approaches when data points are sparsely distributed on a road network.; ------------------------------; 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.23(2015) No.2 (online); DOI http://dx.doi.org/10.2197/ipsjjip.23.163; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Reverse k-nearest neighbor (RkNN) queries on road network distances require long processing times because most conventional algorithms require a k-nearest neighbor (kNN) search on every visited node. This causes a large number of node expansions; therefore, the processing time is drastically increased when data points are sparsely distributed. In this paper, we propose a fast RkNN search algorithm that runs using a simple materialized path view (SMPV). In addition, we adopt an incremental Euclidean restriction strategy for fast kNN queries, the main function in RkNN queries. The SMPV used in our proposed algorithm only constructs an individual partitioned subgraph; therefore, the amount of data is drastically reduced compared to conventional materialized path views (MPVs). According to our experimental results using real road network data, our proposed method achieved a processing time that was 100 times faster than conventional approaches when data points are sparsely distributed on a road network.; ------------------------------; 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.23(2015) No.2 (online); DOI http://dx.doi.org/10.2197/ipsjjip.23.163; ------------------------------
• 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 56, 号 3, 発行日 2015-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764