Extracting output metadata from scientific deep web data sources

Fan Wang, Gagan Agrawal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming the deep web. The popularity of this new medium for data dissemination is leading to new problems in data integration. Particularly, to enable data integration from multiple deep web data sources, one needs to obtain the metadata for each of the data sources. Obtaining the metadata, particularly, the output schema, can be very challenging. This is because, given an input query, many deep web data sources only return a subset of the output schema attributes, i.e, the ones that have a non-NULL value for the corresponding input. In this paper, we propose two approaches, which are the sampling model approach and the mixture model approach, respectively, to efficiently obtain an approximately complete set of output schema attributes from a deep web data source. Our experiments show while each of the above two approaches has limitations, a hybrid strategy, where we combine the two approaches, achieves high recall with good precision for most data sources.

Original languageEnglish (US)
Title of host publicationICDM 2009 - The 9th IEEE International Conference on Data Mining
Pages552-561
Number of pages10
DOIs
StatePublished - 2009
Externally publishedYes
Event9th IEEE International Conference on Data Mining, ICDM 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 9 2009

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference9th IEEE International Conference on Data Mining, ICDM 2009
CountryUnited States
CityMiami, FL
Period12/6/0912/9/09

Keywords

  • Deep web
  • Schema extraction

ASJC Scopus subject areas

  • Engineering(all)

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