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.
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Wang, F., Agrawal, G. (2009). SEEDEEP: A System for Exploring and Querying Scientific Deep Web Data Sources. In: Winslett, M. (eds) Scientific and Statistical Database Management. SSDBM 2009. Lecture Notes in Computer Science, vol 5566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02279-1_6
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DOI: https://doi.org/10.1007/978-3-642-02279-1_6
Publisher Name: Springer, Berlin, Heidelberg
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