{"created":"2023-05-15T12:18:44.591253+00:00","id":8765,"links":{},"metadata":{"_buckets":{"deposit":"44669046-eee7-4968-9d8a-5501cf59999e"},"_deposit":{"created_by":1,"id":"8765","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"8765"},"status":"published"},"_oai":{"id":"oai:kansai-u.repo.nii.ac.jp:00008765","sets":["528:1287:1302"]},"author_link":["17374"],"control_number":"8765","item_7_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2008-12","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"836","bibliographicPageStart":"829","bibliographic_titles":[{"bibliographic_title":"Proceedings. IEEE International Conference on Data Mining Workshops ICDM Workshops 2008"}]}]},"item_7_description_4":{"attribute_name":"概要","attribute_value_mlt":[{"subitem_description":"This purpose of this study is to propose a knowledge-discovery system that can abstract helpful information from character strings representing shopper visits to product sections associated with positive and negative purchasing events by applying character string parsing technologies to stream data describing customer purchasing behavior inside a store. Taking data that traced customers' movements we focus on the number of times customers stop by particular product sections, and by representing those visits in the form of character strings, we propose a way to efficiently handle large stream data. During our experiment, we abstract store-section visiting patterns that characterize customers who purchase a relatively larger volume of items, and are able to show the usefulness of these visiting patterns. In addition, we examine index functions, calculation time, and prediction accuracy, and clarify technological issues warranting further research. In the present study, we demonstrate the feasibility of employing stream data in the marketing field and the usefulness of the employing character parsing techniques.","subitem_description_type":"Other"}]},"item_7_description_5":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, 15-19 December 2008, Pisa, Italy","subitem_description_type":"Other"}]},"item_7_publisher_36":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Institute of Electrical and Electronics Engineers (IEEE)"}]},"item_7_relation_11":{"attribute_name":"ISBN","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"9780769535036","subitem_relation_type_select":"ISBN"}}]},"item_7_rights_15":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"(C) 2008 IEEE.  Reprinted, with permission, from YADA Katsutoshi, Character String Analysis and Customer Path in Stream Data, 12/2008.  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 Kansai University'sproducts 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_7_version_type_19":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNameIdentifiers":[{"affiliationNameIdentifierScheme":"ISNI","affiliationNameIdentifierURI":"http://www.isni.org/isni/"}],"affiliationNames":[{"affiliationNameLang":"ja"}]}],"creatorNames":[{"creatorName":"Yada, Katsutoshi","creatorNameLang":"en"},{"creatorName":"矢田, 勝俊","creatorNameLang":"ja"}],"familyNames":[{"familyName":"矢田","familyNameLang":"ja"},{"familyName":"Yada","familyNameLang":"en"}],"givenNames":[{"givenName":"勝俊","givenNameLang":"ja"},{"givenName":"Katsutoshi","givenNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"17374","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"00298811","nameIdentifierScheme":"e-Rad_Researcher","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/nrid/1000000298811"},{"nameIdentifier":"0000-0003-4094-5327","nameIdentifierScheme":"ORCID iD","nameIdentifierURI":"https://orcid.org/0000-0003-4094-5327"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-05-22"}],"displaytype":"detail","filename":"KU-1100-20081230-10.pdf","filesize":[{"value":"1.0 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"KU-1100-20081230-10.pdf","url":"https://kansai-u.repo.nii.ac.jp/record/8765/files/KU-1100-20081230-10.pdf"},"version_id":"38412848-c05c-4d11-bbd4-a35e2d011ad6"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"character string analysis","subitem_subject_scheme":"Other"},{"subitem_subject":"customer path","subitem_subject_scheme":"Other"},{"subitem_subject":"data mining","subitem_subject_scheme":"Other"},{"subitem_subject":"stream data","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference paper","resourceuri":"http://purl.org/coar/resource_type/c_5794"}]},"item_title":"Character String Analysis and Customer Path in Stream Data","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Character String Analysis and Customer Path in Stream Data","subitem_title_language":"en"}]},"item_type_id":"7","owner":"1","path":["1302"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2012-07-13"},"publish_date":"2012-07-13","publish_status":"0","recid":"8765","relation_version_is_last":true,"title":["Character String Analysis and Customer Path in Stream Data"],"weko_creator_id":"1","weko_shared_id":1},"updated":"2024-10-29T02:18:53.543125+00:00"}