Efficient and effective metasearch for a large number of text databases
Proceedings of the eighth international conference on Information and knowledge management
Towards context-based search engine selection
Proceedings of the 6th international conference on Intelligent user interfaces
Efficient and effective metasearch for text databases incorporating linkages among documents
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
A highly scalable and effective method for metasearch
ACM Transactions on Information Systems (TOIS)
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
Text Retrieval Systems for the Web
Programming and Computing Software
A Methodology to Retrieve Text Documents from Multiple Databases
IEEE Transactions on Knowledge and Data Engineering
A Statistical Method for Estimating the Usefulness of Text Databases
IEEE Transactions on Knowledge and Data Engineering
Heterogeneous image database selection on the web
Journal of Systems and Software
Comparing the performance of collection selection algorithms
ACM Transactions on Information Systems (TOIS)
Information Retrieval with Distributed Databases: Analytic Models of Performance
IEEE Transactions on Parallel and Distributed Systems
Completeness of integrated information sources
Information Systems - Special issue: Data quality in cooperative information systems
Effectively Mining and Using Coverage and Overlap Statistics for Data Integration
IEEE Transactions on Knowledge and Data Engineering
Aggregation of web search engines based on users' preferences in WebFusion
Knowledge-Based Systems
Proceedings of the 16th international conference on World Wide Web
Mining world knowledge for analysis of search engine content
Web Intelligence and Agent Systems
Quality-driven query answering for integrated information systems
Quality-driven query answering for integrated information systems
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In this paper, we present a statistical method to estimate the usefulness of a search engine for any given query. The estimates can be used by a metasearch engine to choose local search engines to invoke. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that the proposed estimation method is quite accurate.