A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing
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...
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iium-319402013-10-10T07:13:41Z http://irep.iium.edu.my/31940/ A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing Alzeini, Haytham I M Hameed, Shihab A. Habaebi, Mohamed Hadi TK5101 Telecommunication. Including telegraphy, radio, radar, television 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. 2013-07-02 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/31940/1/2096.pdf application/pdf en http://irep.iium.edu.my/31940/4/image002.png application/pdf en http://irep.iium.edu.my/31940/6/icomabstracts.pdf Alzeini, Haytham I M and Hameed, Shihab A. and Habaebi, Mohamed Hadi (2013) A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing. In: 5th International Conference on Mechatronics (ICOM'13), 2 – 4 July 2013, Kuala Lumpur, Malaysia. |
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International Islamic University Malaysia |
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Online Access |
language |
English English English |
topic |
TK5101 Telecommunication. Including telegraphy, radio, radar, television |
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TK5101 Telecommunication. Including telegraphy, radio, radar, television Alzeini, Haytham I M Hameed, Shihab A. Habaebi, Mohamed Hadi A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing |
description |
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. |
format |
Conference or Workshop Item |
author |
Alzeini, Haytham I M Hameed, Shihab A. Habaebi, Mohamed Hadi |
author_facet |
Alzeini, Haytham I M Hameed, Shihab A. Habaebi, Mohamed Hadi |
author_sort |
Alzeini, Haytham I M |
title |
A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing |
title_short |
A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing |
title_full |
A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing |
title_fullStr |
A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing |
title_full_unstemmed |
A framework for developing Real-Time OLAP algorithm using multi-core processing and GPU: Heterogeneous computing |
title_sort |
framework for developing real-time olap algorithm using multi-core processing and gpu: heterogeneous computing |
publishDate |
2013 |
url |
http://irep.iium.edu.my/31940/ http://irep.iium.edu.my/31940/1/2096.pdf http://irep.iium.edu.my/31940/4/image002.png http://irep.iium.edu.my/31940/6/icomabstracts.pdf |
first_indexed |
2023-09-18T20:46:04Z |
last_indexed |
2023-09-18T20:46:04Z |
_version_ |
1777409683403833344 |