A highly scalable and effective method for metasearch

  • Authors:

  • Weiyi Meng;Zonghuan Wu;Clement Yu;Zhuogang Li

  • Affiliations:

  • State University of New York at Binghamton;State University of New York at Binghamton;University of Illinois at Chicago;State University of New York at Binghamton

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2001

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Abstract

A metasearch engine is a system that supports unified access to multiple local search engines. Database selection is one of the main challenges in building a large-scale metasearch engine. The problem is to efficiently and accurately determine a small number of potentially useful local search engines to invoke for each user query. In order to enable accurate selection, metadata that reflect the contents of each search engine need to be collected and used. This article proposes a highly scalable and accurate database selection method. This method has several novel features. First, the metadata for representing the contents of all search engines are organized into a single integrated representative. Such a representative yields both computational efficiency and storage efficiency. Second, the new selection method is based on a theory for ranking search engines optimally. Experimental results indicate that this new method is very effective. An operational prototype system has been built based on the proposed approach.