{"_buckets": {"deposit": "f4ba4cad-c5c1-4031-ac2c-559966c553da"}, "_deposit": {"id": "10487", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "10487"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00010487"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2009-03", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "687", "bibliographicPageStart": "676", "bibliographic_titles": [{"bibliographic_title": "Proceedings of the International Conference on Data Engineering (ICDE 2009)"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "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.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/12310"}, {"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://dx.doi.org/10.1109/ICDE.2009.93"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE Computer Society Press"}]}, "item_10_relation_8": {"attribute_name": "ISBN", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "978-0-7695-3545-6", "subitem_relation_type_select": "ISBN"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 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\u2019s 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."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Ishikawa, Yoshiharu"}], "nameIdentifiers": [{"nameIdentifier": "31795", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Iijima, Yuichi"}], "nameIdentifiers": [{"nameIdentifier": "31796", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yu, Jeffrey Xu"}], "nameIdentifiers": [{"nameIdentifier": "31797", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "icde.pdf", "filesize": [{"value": "309.6 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 309600.0, "url": {"label": "icde.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/10487/files/icde.pdf"}, "version_id": "5a85ad99-2ed8-425c-9ed5-489b4eba9efc"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "imprecise locations", "subitem_subject_scheme": "Other"}, {"subitem_subject": "spatial range queries", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Gaussian distributions", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Spatial Range Querying for Gaussian-Based Imprecise Query Objects", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Spatial Range Querying for Gaussian-Based Imprecise Query Objects"}]}, "item_type_id": "10", "owner": "1", "path": ["435/671/672"], "permalink_uri": "http://hdl.handle.net/2237/12310", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2009-10-29"}, "publish_date": "2009-10-29", "publish_status": "0", "recid": "10487", "relation": {}, "relation_version_is_last": true, "title": ["Spatial Range Querying for Gaussian-Based Imprecise Query Objects"], "weko_shared_id": null}