A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing
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}
}
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.
March 9, 2014 by hgpu
