SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An Approach to Integrating Query Refinement in SQL
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Query Refinement in Multimedia Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Supporting top-k join queries in relational databases
The VLDB Journal — The International Journal on Very Large Data Bases
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient IR-style keyword search over relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Probabilistic ranking of database query results
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Answering approximate queries over autonomous web databases
Proceedings of the 18th international conference on World wide web
Supporting queries with imprecise constraints
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Pragmatic correlation analysis for probabilistic ranking over relational data
Expert Systems with Applications: An International Journal
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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.