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SEEDEEP: A System for Exploring and Querying Scientific Deep Web Data Sources
 

Summary: SEEDEEP: A System for Exploring and Querying
Scientific Deep Web Data Sources
Fan Wang Gagan Agrawal
Department of Computer Science and Engineering
Ohio State University, Columbus OH 43210
{wangfa,agrawal}@cse.ohio-state.edu
Abstract. A recent and emerging trend in scientific data dissemination involves
online databases that are hidden behind query forms, thus forming what is re-
ferred 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 key-
word query is answered efficiently and correctly, and how important performance
issues are addressed.
1 Introduction
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 [1].

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences