Abstract
In recent years, effective ranking strategies for relational databases have been extensively studied. Existing approaches have adopted empirical term-weighting strategies called tf×idf (term frequency times inverse document frequency) schemes from the field of information retrieval (IR) without careful consideration of relational model. This paper proposes a novel ranking scheme that exploits the statistical correlations, which represent the underlying semantics of the relational model. We extend Bayesian network models to provide dependence structure in relational databases. Furthermore, a limited assumption of value independence is defined to relax the unrealistic execution cost of the probabilistic model. Experimental results show that our model is competitive in terms of efficiency without losing the quality of query results.
This research was supported by Seoul R&BD Program (WR080951).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, S., Chaudhuri, S., Das, G., Gionis, A.: Automated Ranking of Database Query Results. In: CIDR, pp. 262–268. ACM Press, New York (2003)
Hulgeri, A., Nakhe, C.: Keyword Searching and Browsing in Databases using BANKS. In: ICDE, pp. 431–440. IEEE Press, Washington DC (2002)
Chakrabarti, K., Porkaew, K., Mehrotra, S.: Efficient Query Refinement in Multimedia Databases. In: ICDE, pp. 196–204. IEEE Press, Washington DC (2000)
Chaudhuri, S., Das, G., Hristidis, V., Weikum, G.: Probabilistic ranking of database query results. In: VLDB, pp. 888–899, VLDB Endowment (2000)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS, pp. 102–113. ACM Press, New York (2001)
Hristidis, V., Papakonstantinou, Y.: Discover: keyword search in relational databases. In: VLDB, pp. 670–681, VLDB Endowment (2002)
Hristidis, V., Gravano, L., Papakonstantinou, Y.: Efficient IR-style keyword search over relational databases. In: VLDB, pp. 850–861, VLDB Endowment (2003)
Ilyas, F., Aref, G., Elmagarmid, K.: Supporting top-k join queries in relational databases. The VLDB J. 13(3), 207–221 (2004)
Ortega-Binderberger, M., Chakrabarti, K., Mehrotra, S.: An Approach to Integrating Query Refinement in SQL. In: EDBT, pp. 15–33. ACM Press, New York (2002)
Nambiar, U., Kambhampati, S.: Supporting queries with imprecise constraints. In: AAAI, pp. 1654–1657. AAAI Press, New York (2006)
Ribeiro, B.A., Muntz, R.: A belief network model for IR. In: SIGIR, pp. 253–260. ACM Press, New York (1996)
Meng, X., Ma, Z.M., Yan, L.: Answering approximate queries over autonomous web databases. In: WWW, pp. 1021–1030. ACM Press, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, J., Lee, Sg. (2011). Exploiting Correlation to Rank Database Query Results. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20152-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-20152-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20151-6
Online ISBN: 978-3-642-20152-3
eBook Packages: Computer ScienceComputer Science (R0)