Seedeep: A system for exploring and querying scientific deep web data sources

Fan Wang, Gagan Agrawal

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

1 Scopus citations

Abstract

A recent and emerging trend in scientific data dissemination involves online databases that are hidden behind query forms, thus forming what is referred to as the deep web. In this paper, we propose SEEDEEP, a System for Exploring and quErying scientific DEEP web data sources. SEEDEEP is able to automatically mine deep web data source schemas, integrate heterogeneous data sources, answer cross-source keyword queries, and incorporates features like caching and fault-tolerance. Currently, SEEDEEP integrates 16 deep web data sources in the biological domain. We demonstrate how an integrated model for correlated deep web data sources is constructed, how a complex cross-source keyword query is answered efficiently and correctly, and how important performance issues are addressed.

Original languageEnglish (US)
Title of host publicationScientific and Statistical Database Management - 21st International Conference, SSDBM 2009, Proceedings
Pages74-82
Number of pages9
DOIs
StatePublished - 2009
Externally publishedYes
Event21st International Conference on Scientific and Statistical Database Management, SSDBM 2009 - New Orleans, LA, United States
Duration: Jun 2 2009Jun 4 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5566 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Scientific and Statistical Database Management, SSDBM 2009
CountryUnited States
CityNew Orleans, LA
Period6/2/096/4/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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