Please use this identifier to cite or link to this item:
https://doi.org/10.1145/1376616.1376707
DC Field | Value | |
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dc.title | A Graph Method for Keyword-based Selection of the top-K Databases | |
dc.contributor.author | Vu, Q.H. | |
dc.contributor.author | Ooi, B.C. | |
dc.contributor.author | Papadias, D. | |
dc.contributor.author | Tung, A.K.H. | |
dc.date.accessioned | 2013-07-04T08:03:37Z | |
dc.date.available | 2013-07-04T08:03:37Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Vu, Q.H.,Ooi, B.C.,Papadias, D.,Tung, A.K.H. (2008). A Graph Method for Keyword-based Selection of the top-K Databases. Proceedings of the ACM SIGMOD International Conference on Management of Data : 915-926. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1376616.1376707" target="_blank">https://doi.org/10.1145/1376616.1376707</a> | |
dc.identifier.isbn | 9781605581026 | |
dc.identifier.issn | 07308078 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/40408 | |
dc.description.abstract | While database management systems offer a comprehensive solution to data storage, they require deep knowledge of the schema, as well as the data manipulation language, in order to perform effective retrieval. Since these requirements pose a problem to lay or occasional users, several methods incorporate keyword search (KS) into relational databases. However, most of the existing techniques focus on querying a single DBMS. On the other hand, the proliferation of distributed databases in several conventional and emerging applications necessitates the support for keyword-based data sharing and querying over multiple DMBSs. In order to avoid the high cost of searching in numerous, potentially irrelevant, databases in such systems, we propose G-KS, a novel method for selecting the top-K candidates based on their potential to contain results for a given query. G-KS summarizes each database by a keyword relationship graph, where nodes represent terms and edges describe relationships between them. Keyword relationship graphs are utilized for computing the similarity between each database and a KS query, so that, during query processing, only the most promising databases are searched. An extensive experimental evaluation demonstrates that G-KS outperforms the current state-of-the-art technique on all aspects, including precision, recall, efficiency, space overhead and flexibility of accommodating different semantics. Copyright 2008 ACM. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1376616.1376707 | |
dc.source | Scopus | |
dc.subject | Database summary | |
dc.subject | Distributed databases | |
dc.subject | Graph | |
dc.subject | Information retrieval | |
dc.subject | Keyword search | |
dc.subject | Relational databases | |
dc.type | Conference Paper | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.description.doi | 10.1145/1376616.1376707 | |
dc.description.sourcetitle | Proceedings of the ACM SIGMOD International Conference on Management of Data | |
dc.description.page | 915-926 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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