Size Estimation of Non-Cooperative Data Collections

Mohammadreza Khelghati, Djoerd Hiemstra, Maurice van Keulen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)
15 Downloads (Pure)


With the increasing amount of data in deep web sources (hidden from general search engines behind web forms), ac- cessing this data has gained more attention. In the algo- rithms applied for this purpose, it is the knowledge of a data source size that enables the algorithms to make accurate de- cisions in stopping the crawling or sampling processes which can be so costly in some cases [14]. This tendency to know the sizes of data sources is increased by the competition among businesses on the Web in which the data coverage is critical. In the context of quality assessment of search engines [7], search engine selection in the federated search engines, and in the resource/collection selection in the dis- tributed search field [19], this information is also helpful. In addition, it can give an insight over some useful statistics for public sectors like governments. In any of these mentioned scenarios, in the case of facing a non-cooperative collection which does not publish its information, the size has to be estimated [17]. In this paper, the suggested approaches for this purpose in the literature are categorized and reviewed. The most recent approaches are implemented and compared in a real environment. Finally, four methods based on the modification of the available techniques are introduced and evaluated. In one of the modifications, the estimations from other approaches could be improved ranging from 35 to 65 percent.
Original languageUndefined
Title of host publicationProceedings of the 14th International Conference on Information Integration and Web-based Applications & Services (iiWAS2012)
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)978-1-4503-1306-3
Publication statusPublished - 2012


  • Pool-Based Size Es-timation
  • Estimation Bias
  • Regres-sion Equations
  • Size Estimation
  • METIS-289755
  • CR-H.3.3
  • query-based sampling
  • EWI-22426
  • Deep Web
  • Stochastic Simulation

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