Abstract
As organisations become globalised and geographically distributed, high performance computing resources become inaccessible from remote branches of the organisation. On the other hand, companies with excess high performance computational resources may wish to leverage their investments and sell access/services to other smaller companies. In this paper, we propose a three-tier object-oriented NetSolution software architecture which enables high performance resources to be made available anywhere and on any platform, via the Internet. We present a case study of distributed data mining and show how the architecture can be applied. Finally, we present two case studies of the application of the NetSolution architecture in the field of distributed data mining and risk management.
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J. Chattratichat, J. Darlington, M. Ghanem, Y. Guo, H. Hüning, M. Köhler, J. Sutiwaraphun, H. W. To, and D. Yang. Large scale data mining: Challenges and responses. In Proceedings of Third International Conference on Knowledge Discovery and Data Mining, pages 143–146, 1997.
J. Chattratichat, J. Darlington, C. C. Pantellides, B. Rustem, and B.A. Tanyi. Parallel nonlinear optimisation for decision making under uncertainty. In Proceeding of the Sixth Parallel Computing Workshop, Kawasaki, Japan, November 12–13, 1996, 1996.
P. Chan and S. Stolfo. Towards parallel and distributed learning by meta-learning. In Working Notes AAAI Workshop on Knowledge Discovery in Databases, pages 227–240. AAAI, 1993.
P. Chan and S. Stolfo. On the accuracy of meta-learning for scalable data mining. Journal of Intelligent Information System, 8:5–28, 1996.
P. K. Chan and S. J. Stolfo. Sharing learned models among remote database partitions by local meta-learning. In E. Simoudis, J. Han, and U. Fayyad, editors, The Second International Conference on Knowledge Discovery and Data Mining, pages 2–7. AAAI Press, 1996.
U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery: An overview. In U. M. Fayyad, G. Piatetesky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining. MIT Press, 1996.
Yike Guo and Richard Mill. Parallelising bayesian classification. In Proceedings of the Seventh Parallel Computing Workshop, Australian National University, Canberra, September 25–26, 1997, 1997.
K. A. De Jong, W. M. Spears, and D. F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161–188, 1993.
C. J. Merz and P. M. Murphy. “uci repository of machine learning databases”. University of California, Department of Information and Computer Science, http://www.ics.uci.edu/~mlearn/MLRepository.html,1996.
Thomas J. Mowbray and Ron Zahavi. The Essential Corba-System Integaration Using Distributed Objects. John Wiley & Sons, Inc, 1995.
Stefan Rüger. Parallel self-organising maps. In Proceedings of the Seventh Parallel Computing Workshop, Australian National University, Canberra, September 25–26, 1997, 1997.
William Stallings. Network and InlerNetwork Securiy. Prentice Hall, 1995.
Hannu Toivonen. Discovery of Frequent Patterns in Large Data Collections. PhD thesis, Department of Computer Science, University of Finland, 1996.
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Chattratichat, J. et al. (1998). A software architecture for deploying high performance solution on the Internet. In: Sloot, P., Bubak, M., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1998. Lecture Notes in Computer Science, vol 1401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037182
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DOI: https://doi.org/10.1007/BFb0037182
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