Abstract
Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying database schemas. There would be a huge number of valid results for a keyword query in a large database. However, only the top 10 or 20 most relevant matches for the keyword query –according to some definition of “Relevance”– are generally of interest. In this paper, we propose an efficient method for answering top-k keyword queries over relational databases. The proposed method is built on an existing scheme of keyword search on relational data streams, but incorporates the ranking mechanisms into the query processing methods and makes two improvements to support top-k keyword search in relational databases. Experimental results validate the effectiveness and efficiency of the proposed method.
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Xu, Y., Guan, J., Ishikawa, Y. (2012). Scalable Top-k Keyword Search in Relational Databases. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29035-0_5
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DOI: https://doi.org/10.1007/978-3-642-29035-0_5
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