a parallel multi-scale region outlier mining algorithm for meteorological data
The increase use of high dimensional, geographically distributed rich and massive meteorological data poses an increasing scientific challenge in efficient outlier mining. Properties in such meteorological data are observed to fluctuate in spatial synchrony. Capturing this spatial variation at different spatial scales requires a multi-resolution analysis. In this paper, we develop an algorithm for region outlier detection at different scales using the multi-resolution feature of wavelet analysis. Another challenge of meteorological data mining is that the data size is huge to accommodate different resolutions and number of samples varies with the spatial scales. This motivated us to design a load adaptive parallel algorithm for outlier detection which can maintain good scalability for all spatial scales. Our algorithm has been implemented on high-performance computing architecture and evaluated on real-world meteorological data.
region outlier mining parallel wavelet transform