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A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

H.I. Alzeini, Sh.A. Hameed, M.H. Habaebi
Electrical and Computer Department, IIUM, PO: 53100, Jalan Gombak, Kuala-Lumpur, Malaysia
arXiv:1402.3781 [cs.DC], (16 Feb 2014)

@article{2014arXiv1402.3781A,

   author={Alzeini}, H. and {Hameed}, S.~A and {Habaebi}, M.},

   title={"{A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1402.3781},

   primaryClass={"cs.DC"},

   keywords={Computer Science – Distributed, Parallel, and Cluster Computing},

   year={2014},

   month={feb},

   adsurl={http://adsabs.harvard.edu/abs/2014arXiv1402.3781A},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real-Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses.
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