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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/3244
Title: A graph method for keyword-based selection of the top-K databases
Authors: Vu, Quang Hieu
Ooi, Beng Chin
Papadias, Dimitris
Tung, Anthony K. H.
Keywords: Database summary
Keyword relationship graph
Relational databases
Issue Date: 2008
Citation: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, 9-12 June 2008, Vancouver, Canada, p. 915-926
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.
Rights: © ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, {9-12 June 2008, Vancouver, Canada} http://doi.acm.org/10.1145/1376616.1376707
URI: http://hdl.handle.net/1783.1/3244
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