Evaluating Relational Ranking Queries Involving both Text Attributes and Numeric Attributes
- 1 Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China
- 2 Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China
- 3 Department of Foreign Language Teaching and Research, Hebei University, Baoding, Hebei 071002, China
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.
- M. Carey, and D. Kossmann, “On saying ‘enough al-ready!’ in SQL,” SIGMOD 1997 Proceedings ACM in-ternational conference on management of data, Vol. 26 No. 2, 1997, pp. 219-230.
- I. F Ilyas, G. Beskales, and M. A. Soliman, “A survey of top-k query processing techniques in relational database systems,” ACM Com-puting Surveys, Vol. 40, No. 4, 2008, Article 11.
- L. Zhu, W. Meng, C. Liu, W. Yang, and D. Liu, “Processing top-N relational queries by learning,” Journal of Intelligent Information Systems. Vol.34, No.1, 2010, pp.21-55, doi:10.1007/s10844-009-0078-7.
- IPUMS Census Database, “ipums.la.99.gz”, 1999, http://kdd.ics.uci.edu/databases/ipums/ipums.html
- T. Pedersen, “WordNet::Similarity,” 2008, http://www.d.umn.edu/~tpederse/similarity.html
- L. Zhu, Q. Ma, C. Liu, G. Mao and W. Yang. “Seman-tic-distance based evaluation of ranking queries over relational databases,” Journal of Intelligent Information Systems, Vol. 35, No. 3, 2010, pp. 415-445.
- D. Buscaldi, P. Rosso, and A. E. Sanchis, “A wordnet-based query expansion method for geographical information retrieval,” Working Notes for the CLEF Workshop, Vienna Austria, 2005.
- S. Das, E. Chong, G. I. Eadon, and J. Srinivasan, “Supporting ontology-based semantic matching in RDBMS,” Proceedings of the Thirtieth International Conference on Very Large Data Bases (VLDB’04), Toronto, Canada, 2004, pp. 1054-1065.
- J. Zhang, Z. Peng, S. Wang and H. Niehang, “Si-SEEKER: Ontology-based semantic search over databases,” Knowledge Science, Engineering and Management, Guilin, China, Vol. 4092, 2006, pp. 599-611.
- V. Hristidis, L. Gravano, and Y. Papakonstantinou, “Efficient IR-style keyword search over relational databases,” In Proceedings of 29th International Conference on Very Large Data Bases (VLDB’03), Berlin, Germany, 2003, pp. 850-861.
- F. Liu, C. Yu, W. Meng and A. Chowdhury, “Effective Keyword Search in Relational Database,” 26th ACM SIGMOD/PODS international Conference on Manage-ment of Data/Principle of Database Systems, Chicago, Illinois, USA, 2006, pp. 563-574.
- L. Zhu, Y. Zhu, and Q. Ma, “Chinese Keyword Search over Relational Databases,” World Conference on Science and Engineering (WCSE), Wuhan, China, Vol. 1, 2010, pp. 217 – 220.