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  1. M320 情報基盤センター
  2. M320a 雑誌掲載論文
  3. 学術雑誌

Spatial Range Querying for Gaussian-Based Imprecise Query Objects

http://hdl.handle.net/2237/12310
http://hdl.handle.net/2237/12310
ebec5018-506e-4972-ab92-3879691b9458
名前 / ファイル ライセンス アクション
icde.pdf icde.pdf (309.6 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2009-10-29
タイトル
タイトル Spatial Range Querying for Gaussian-Based Imprecise Query Objects
言語 en
著者 Ishikawa, Yoshiharu

× Ishikawa, Yoshiharu

WEKO 31795

en Ishikawa, Yoshiharu

Search repository
Iijima, Yuichi

× Iijima, Yuichi

WEKO 31796

en Iijima, Yuichi

Search repository
Yu, Jeffrey Xu

× Yu, Jeffrey Xu

WEKO 31797

en Yu, Jeffrey Xu

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 Copyright (c) 2009 IEEE. Reprinted from Proceedings of the International Conference on Data Engineering (ICDE 2009), 2009, pp. 676-687. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
キーワード
主題Scheme Other
主題 imprecise locations
キーワード
主題Scheme Other
主題 spatial range queries
キーワード
主題Scheme Other
主題 Gaussian distributions
抄録
内容記述 In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In this paper, we present query processing methods for spatial databases in which the position of the query object is imprecisely specified by a probability density function based on a Gaussian distribution. We define the notion of a probabilistic range query by extending the traditional notion of a spatial range query and present three strategies for query processing. Since the qualification probability evaluation of target objects requires numerical integration by a method such as the Monte Carlo method, reduction of the number of candidate objects that should be evaluated has a large impact on query performance. We compare three strategies and their combinations in terms of the experiments and evaluate their effectiveness.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 IEEE Computer Society Press
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
ISBN
関連タイプ isPartOf
識別子タイプ ISBN
関連識別子 978-0-7695-3545-6
書誌情報 en : Proceedings of the International Conference on Data Engineering (ICDE 2009)

p. 676-687, 発行日 2009-03
フォーマット
application/pdf
著者版フラグ
値 publisher
URI
識別子 http://hdl.handle.net/2237/12310
識別子タイプ HDL
URI
識別子 http://dx.doi.org/10.1109/ICDE.2009.93
識別子タイプ DOI
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