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Integrated Performance and Visualization Enhancements of OLAP Using Growing Self Organizing Neural Networks

Muhammad Usman1, Sohail Asghar2, and Simon Fong3
1. Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
2. Mohammad Ali Jinnah University, Islamabad, Pakistan
3. University of Macau, Taipa, Macau SAR
Abstract—OLAP performance and its data visualization can be improved using different types of enhancement techniques. Previous research has taken two separate directions in OLAP performance improvement and visualization enhancement respectively. Some recent works have shown the benefits of combining OLAP and Data Mining. Our previous work presents an architecture for the enhancement of OLAP functionality by integrating OLAP and Data Mining. In this paper, we proposed a novel architecture that not only overcomes the existing limitations, but also provides a way for an integrated enhancement of performance and visualization using self organizing neural network. We have developed a prototype and validated the proposed architecture using real-life data sets. Experimental results show that cube construction time and its interactive data visualization capability can be improved remarkably. By integrating enhanced OLAP with data mining system a higher degree of enhancement is achieved which makes significant advancement in the modern OLAP systems.

Index Terms— Clustering, Data Mining, GSOM, Multidimensional Data, OLAP, Performance Enhancement, Visualization Techniques

Cite: Muhammad Usman, Sohail Asghar, and Simon Fong, "Integrated Performance and Visualization Enhancements of OLAP Using Growing Self Organizing Neural Networks," Journal of Advances in Information Technology, Vol. 1, No. 1, pp. 26-37, February, 2010.doi:10.4304/jait.1.1.26-37
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