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|Title:||A Graph Method for Keyword-based Selection of the top-K Databases||Authors:||Vu, Q.H.
|Issue Date:||2008||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. https://doi.org/10.1145/1376616.1376707||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.||Source Title:||Proceedings of the ACM SIGMOD International Conference on Management of Data||URI:||http://scholarbank.nus.edu.sg/handle/10635/40408||ISBN:||9781605581026||ISSN:||07308078||DOI:||10.1145/1376616.1376707|
|Appears in Collections:||Staff Publications|
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