Abstract
Increasingly, biological data is being shared over the deep web. Many biological queries can only be answered by successively searching a number of distinct web-sites. This paper introduces a system that exploits parallelization for accelerating search over multiple deep web data sources. An interactive, two-stage multi-threading system is developed to achieve task parallelization, thread parallelization, and pipelined parallelization. We show the effectiveness of our system by considering a number of queries involving SNP datasets. We show that most of the queries can be accelerated significantly by exploiting these three forms of parallelism.
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Liu, T., Wang, F., Agrawal, G. (2009). Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources. In: Paton, N.W., Missier, P., Hedeler, C. (eds) Data Integration in the Life Sciences. DILS 2009. Lecture Notes in Computer Science(), vol 5647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02879-3_12
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DOI: https://doi.org/10.1007/978-3-642-02879-3_12
Publisher Name: Springer, Berlin, Heidelberg
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