Extended Boolean information retrieval
Communications of the ACM
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 17th International Conference on Data Engineering
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
RAF: an activation framework for refining similarity queries using learning techniques
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
User feedback based query refinement by exploiting skyline operator
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
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Similarity retrieval have been widely used in many practical search applications. A similarity query model can be viewed as a logical combination of a set of similarity predicates. A user can initialize a query model, but model parameters or the model itself may be inadequately specified. As a result, a retrieval system cannot guarantee that it has presented all the relevant tuples to the user. In this paper, we propose a framework that integrates the similarity retrieval and skyline exploration. Using the relevance feedback as a way to constrain the search space, our framework can intelligently explore only a necessary portion of data that contains all the relevant tuples. Our framework is also flexible enough to incorporate model refinement techniques to retrieving relevant results as early as possible.