Detecting Significant Locations from Raw GPS Data Using Random Space Partitioning

• Item type: JInfP(1)
• 公開日: 2012-07-15
• タイトル: Detecting Significant Locations from Raw GPS Data Using Random Space Partitioning
• タイトル / 言語: en
• タイトル: Detecting Significant Locations from Raw GPS Data Using Random Space Partitioning
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [Special Issue on ICT Activating Society] significant locations, GPS, random partitioning, LSH
• 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: System Platforms Research Laboratories, NEC Corporation
• 著者所属: System Platforms Research Laboratories, NEC Corporation
• 著者所属: System Platforms Research Laboratories, NEC Corporation
• 著者所属: System Platforms Research Laboratories, NEC Corporation
• 著者所属: RCAST, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): System Platforms Research Laboratories, NEC Corporation
• 著者所属(英): en
• 著者所属(英): System Platforms Research Laboratories, NEC Corporation
• 著者所属(英): en
• 著者所属(英): System Platforms Research Laboratories, NEC Corporation
• 著者所属(英): en
• 著者所属(英): System Platforms Research Laboratories, NEC Corporation
• 著者所属(英): en
• 著者所属(英): RCAST, The University of Tokyo
• 著者名: Nobuharu, Kami; Teruyuki, Baba; Satoshi, Ikeda; Takashi, Yoshikawa; Hiroyuki, Morikawa
• 著者名(英): Nobuharu, Kami; Teruyuki, Baba; Satoshi, Ikeda; Takashi, Yoshikawa; Hiroyuki, Morikawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Most current algorithms compare spatial/temporal variables with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori, and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, they do not often scale in response to increase in system size since direct distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality-sensitive hashing. Theoretical analysis and evaluations show that significant locations are accurately detected with a loose parameter setting even under high noise levels.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Most current algorithms compare spatial/temporal variables with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori, and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, they do not often scale in response to increase in system size since direct distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality-sensitive hashing. Theoretical analysis and evaluations show that significant locations are accurately detected with a loose parameter setting even under high noise levels.
• 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: AA00700121
• 書誌情報: Journal of information processing; 巻 20, 号 3, p. 757-766, 発行日 2012-07-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6652
• 出版者 / 言語: ja
• 出版者: 情報処理学会