A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1807.08099v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
Big data applications involving the analysis of large datasets becomes a critical part of many emerging paradigms such as smart cities, social networks and modern security systems. Cloud computing has developed as a mainstream for hosting big data applications by its ability to provide the illusion of infinite resources. However, harnessing cloud resources for large-scale big data computation is application specific to a large extent. In this paper, we propose a system for large-scale<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.08099v1">arXiv:1807.08099v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/52m2ltcd6fathkgduhsheqfydy">fatcat:52m2ltcd6fathkgduhsheqfydy</a> </span>
more »... t matching application using Aneka, a platform or developing scalable applications on the Cloud. We present the design and implementation of our proposed system and conduct experiments to evaluate its performance using resources from Microsoft Azure. Experimental results demonstrate that matching time for biometric information such as fingerprints in large-scale databases can be reduced substantially using our proposed system.
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