Abstract
Nowadays E-Business and E-Science are generating plenty of datasets. These datasets are heterogeneous and geographically distributed. There are major challenges involved in the efficient extracting useful knowledge from the datasets. This paper proposes a Grid based data mining architecture for Grid based Urban Public Transport Decision Support System (GUPTDSS). It discusses three main topics: process of parallel algorithm; deployment, invoking and scheduling of Grid based data mining service; data sources distribution scenarios and data access. To evaluate the efficiency of the proposed system, an example of traffic flow classification is presented.
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Gong, J., Wang, Y., Song, H., Chen, X., Zhang, Q. (2009). The Research and Implementation of Grid Based Data Mining Architecture. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_110
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DOI: https://doi.org/10.1007/978-3-642-01510-6_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
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