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High-performance computing for data analytics

Perrin, Dimitri orcid logoORCID: 0000-0002-4007-5256, Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113, Crane, Martin orcid logoORCID: 0000-0001-7598-3126, Ruskin, Heather J. orcid logoORCID: 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.

Abstract
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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Drugs, Analytical models; Data models; Computational modeling; Polymers; Coatings
Subjects:Medical Sciences > Pharmacology
Computer Science > Computational complexity
DCU Faculties and Centres:Research Institutes and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings - IEEE International Symposium on Distributed Simulation and Real-Time Applications. . IEEE Computer Society.
Publisher:IEEE Computer Society
Official URL:http://dx.doi.org/10.1109/DS-RT.2012.41
Copyright Information:© 2012 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science, Engineering and Technology (IRCSET), co-funded by Marie Curie Actions under FP7, “Enterprise Partnership Scheme & Sigmoid Pharma Ltd
ID Code:21697
Deposited On:01 Feb 2017 16:22 by Thomas Murtagh . Last Modified 03 Oct 2018 11:58
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