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In this paper, a flow data management and analysis system that can automatically extract features and present summaries of flow fields for the researcher was presented by applying information extraction and mining techniques. The informative vortex features were extracted by using a content-based feature extractor. Shot detection was implemented on the basis of a Maximum-Block-Difference method and global clustering was implemented with semi-Hausdorff distance measure. On the other hand, the frequent patterns in data sequences and the hidden relations among visual features were also discovered by the application of mining techniques. The implementation of this system is believed to benefit both the information scientist in the context of knowledge discovery and at the same time help develop a good data management system for the fluid dynamist to better deal with the flow data.

Keywords: data mining, clustering, feature extraction, database, vortex flow

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Received March 14, 2005; revised June 15, 2005; accepted August 24, 2005.
Communicated by Ming-Syan Chen.
^*This paper was partially supported by the National Science Council of Taiwan, R.O.C., under grant No. 90-2213-E-009-130.