CoBase: a scalable and extensible cooperative information system
Journal of Intelligent Information Systems - Special issue on intelligent integration of information
Automatic discovery of language models for text databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
FLEX: A Tolerant and Cooperative User Interface to Databases
IEEE Transactions on Knowledge and Data Engineering
Cooperative Answering through Controlled Query Relaxation
IEEE Expert: Intelligent Systems and Their Applications
Generalization and a Framework for Query Modification
Proceedings of the Sixth International Conference on Data Engineering
Efficient Discovery of Functional and Approximate Dependencies Using Partitions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Machine learning for online query relaxation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining approximate functional dependencies and concept similarities to answer imprecise queries
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
Answering Imprecise Queries over Autonomous Web Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Relaxing join and selection queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Online query relaxation via Bayesian causal structures discovery
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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The information on the deep web is much more abundant than the surface web, so it is important to make the best use of it. However, in the process of query, it is difficult to avoid the so-called failed queries that make no result. Instead of notifying the user that there is no result, it is more cooperative to modify the raw query to return non-empty result set. Inspired by the observations on the deep web, this paper presents a query relaxation solution. Firstly, it applies the technique of query probing to obtain data samples from the underlying deep web databases. Based on these data samples, the important degree of attributes are obtained by employing approximate functional dependence. Secondly, the databases matching the query better are chosen and divided into some groups in terms of their schemas. Then the groups are organized into a directed acyclic graph called database relationship graph (DRG) to implement query relaxation. Finally, it returns some results satisfying the query better. We have conducted experiments to demonstrate the feasibility and the efficiency of the solution.