Discovery of similarity computations of search engines
Proceedings of the ninth international conference on Information and knowledge management
Towards a highly-scalable and effective metasearch engine
Proceedings of the 10th international conference on World Wide Web
Probe, count, and classify: categorizing hidden web databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A highly scalable and effective method for metasearch
ACM Transactions on Information Systems (TOIS)
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
QProber: A system for automatic classification of hidden-Web databases
ACM Transactions on Information Systems (TOIS)
Probe, Cluster, and Discover: Focused Extraction of QA-Pagelets from the Deep Web
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Discovering and ranking web services with BASIL: a personalized approach with biased focus
Proceedings of the 2nd international conference on Service oriented computing
Distributed query sampling: a quality-conscious approach
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Classification-aware hidden-web text database selection
ACM Transactions on Information Systems (TOIS)
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As the number of text retrieval systems (search engines) grows rapidly on the World Wide Web, there is an increasing need to build search brokers (metasearch engines) on top of them. Often, the task of building an effective and efficient metasearch engine is hindered by the heterogeneities among the underlying local search engines. In this paper, we first analyze the impact of various heterogeneities on building a metasearch engine. We then present some techniques that can be used to detect the most prominent heterogeneities among multiple search engines. Applications of utilizing the detected heterogeneities in building better metasearch engines will be provided.