Abstract
There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by exploiting the correlations among their attribute values. Given a query, each attribute value is assigned a score according to mutual occurrences with the query and its distribution status in the columns of the attribute. These attribute value scores are aggregated to get a final score for an object. Furthermore, a concept vector is proposed to provide a synopsis of the attribute value in a given database. A concept vector is utilized to get the similar objects. Experimental results demonstrate the performance of our ranking method, RAVC (Ranking with Attribute Value Correlation), in terms of search quality and efficiency.
This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency). (grant number NIPA-2009-C1090-0902-0031).
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Park, J., Lee, Sg. (2010). Ranking Objects Based on Attribute Value Correlation. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15251-1_28
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DOI: https://doi.org/10.1007/978-3-642-15251-1_28
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