{"_buckets": {"deposit": "57790874-522c-42cd-8d20-8a81173a74cd"}, "_deposit": {"created_by": 45, "id": "5401", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5401"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5401", "sets": ["1596102391527", "user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Answering Top-k Keyword Queries on Relational Databases", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Keyword search in relational databases allows the user to search information without knowing database schema and using structural query language. As results needed by user are assembled from connected tuples of multiple relations, ranking keyword queries are needed to retrieve relevant results. For a given keyword query, we first generate candidate networks and also produce connected tuple trees according to the generated candidate networks by reducing the size of intermediate joining results. We then model the generated connected tuple trees as a document and evaluate score for each document to estimate its relevance. Finally, we retrieve top-k keyword queries by ranking the results. In this paper, we propose a new ranking method based on virtual document. We also propose Top-k CTT algorithm by using the frequency threshold value. The experimental results are shown by comparison of the proposed ranking method and the previous ranking methods on IMDB and DBLP datasets."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Candidate Network"}, {"interim": "Connected Tuple Tree"}, {"interim": "Top-k"}, {"interim": "Keyword Query"}, {"interim": "Keyword Search"}, {"interim": "Relational Databases"}]}, "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": "Answering Top-k Keyword Queries on Relational Databases.pdf", "filesize": [{"value": "643 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 643000.0, "url": {"url": "https://meral.edu.mm/record/5401/files/Answering Top-k Keyword Queries on Relational Databases.pdf"}, "version_id": "77dc0ed9-0078-42dd-ac91-7db76684e0ee"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_journal_title": "IJIRR"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Myint Myint Thein"}, {"subitem_authors_fullname": "Mie Mie Su Thwin"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2012-07-01"}, "item_title": "Answering Top-k Keyword Queries on Relational Databases", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005401", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-16"}, "publish_date": "2020-09-16", "publish_status": "0", "recid": "5401", "relation": {}, "relation_version_is_last": true, "title": ["Answering Top-k Keyword Queries on Relational Databases"], "weko_shared_id": -1}