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High-performance computing for data analytics
Perrin, Dimitri; Bezbradica, Marija; Crane, Martin; Ruskin, Heather J.; Duhamel, Christophe
One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques.
Keyword(s): Pharmacology; Computational complexity; Drugs, Analytical models; Data models; Computational modeling; Polymers; Coatings
Publication Date:
2012
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Perrin, Dimitri ORCID: 0000-0002-4007-5256 <https://orcid.org/0000-0002-4007-5256>, Bezbradica, Marija ORCID: 0000-0001-9366-5113 <https://orcid.org/0000-0001-9366-5113>, Crane, Martin ORCID: 0000-0001-7598-3126 <https://orcid.org/0000-0001-7598-3126>, Ruskin, Heather J. ORCID: 0000-0001-7101-2242 <https://orcid.org/0000-0001-7101-2242> and Duhamel, Christophe (2012) High-performance computing for data analytics. In: IEEE/ACM 16th International Symposium on Distributed Simulation and Real Time Applications, 25-27 Dec 2012, Dublin, Ireland.
Publisher(s): IEEE Computer Society
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/21697/1/882E.tmp.pdf,
http://dx.doi.org/10.1109/DS-RT.2012.41
First Indexed: 2017-02-02 05:58:20 Last Updated: 2019-02-09 06:12:06