Probabilistic ranking for relational databases based on correlations

Authors:
Jaehui Park;Sang-goo Lee
Affiliations:
Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea
Venue:
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
Year:
2010

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Abstract

This paper proposes a ranking method to exploit statistical correlations among pairs of attribute values in relational databases. For a given query, the correlations of the query are aggregated with each of the attribute values in a tuple to estimate the relevance of that tuple to the query. We extend Bayesian network models to provide a probabilistic ranking function based on a limited assumption of value independence. Experimental results show that our model improves the retrieval effectiveness on real datasets and has a reasonable query processing time compared to related work.