Evaluating Relational Ranking Queries Involving both Text Attributes and Numeric Attributes

Liang Zhu, Zhaoliang Xie, Qin Ma

Department of Foreign Language Teaching and Research, Hebei University, Baoding, Hebei 071002, China.
Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China.

DOI: 10.4236/jsea.2012.512B018                

Abstract

In many database applications, ranking queries may reference both text and numeric attributes, where the ranking functions are based on both semantic distances/similarities for text attributes and numeric distances for numeric attributes. In this paper, we propose a new method for evaluating such type of ranking queries over a relational database. By statistics and training, this method builds a mechanism that combines the semantic and numeric distances, and the mechanism can be used to balance the effects of text attributes and numeric attributes on matching a given query and tuples in database search. The basic idea of the method is to create an index based on WordNet to expand the tuple words semantically for text attributes and on the information of numeric attributes. The candidate results for a query are retrieved by the index and a simple SQL selection statement, and then top-N answers are obtained. The results of extensive experiments indicate that the performance of this new strategy is efficient and effective.

Keywords

Relational Database, Ranking Query, Semantic Distance, Numeric Distance, WordNet

Conflicts of Interest

The authors declare no conflicts of interest.

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