{"_buckets": {"deposit": "09e882f4-87c3-40aa-94ae-8a3815270e59"}, "_deposit": {"created_by": 45, "id": "5405", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5405"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5405", "sets": ["1596102391527", "user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Improved Ranking Method for Keyword Queries on Relational Database", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Keyword search is an easy and potentially\neffective way to find information that is stored in\nrelational database for ordinary users or web\nusers. As results needed by user are assembled\nfrom joining tuples of multiple relations, ranking\nkeyword queries are needed to retrieve relevant\nresults by a given keyword query. For a given\nkeyword query, we first generate a set of joining\ntuples, such as candidate networks (CNs). We\nthen model the generated CN as a document. We\nevaluate the score for each document to estimate\nits relevance to a given keyword query. Finally,\nwe rank the relevant queries by using each\nevaluated score as high as possible. In this\npaper, we propose a new ranking method by\nadapting existing IR scoring techniques based on\nthe virtual document. We evaluate the proposed\nranking method on DBLP dataset. The\nexperimental results are shown by comparison of\nthe proposed ranking method and the previous\nIR ranking method."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Candidate Network"}, {"interim": "IR ranking method"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-09-16"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Improved Ranking Method for Keyword Queries on Relational Database.pdf", "filesize": [{"value": "207 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 207000.0, "url": {"url": "https://meral.edu.mm/record/5405/files/Improved Ranking Method for Keyword Queries on Relational Database.pdf"}, "version_id": "f50bde54-3826-46f9-b43b-96754dcea209"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICCA 2012", "subitem_c_date": "28-29 February, 2012", "subitem_conference_title": "TENTH INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS", "subitem_place": "Yangon, Myanmar", "subitem_website": "https://www.ucsy.edu.mm/Goiccahome.do"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Myint Myint Thein"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2012-02-29"}, "item_title": "Improved Ranking Method for Keyword Queries on Relational Database", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005405", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-16"}, "publish_date": "2020-09-16", "publish_status": "0", "recid": "5405", "relation": {}, "relation_version_is_last": true, "title": ["Improved Ranking Method for Keyword Queries on Relational Database"], "weko_shared_id": -1}