Scalable exploratory data mining of distributed geoscientific data

Abstract

Geoscience studies produce data from various observations, experiments, and simulations at an enormous rate. Exploratory data mining extracts "content information" from massive geoscientific datasets to extract knowledge and provide a compact summary of the dataset. In this paper, we discuss how database query processing and distributed object management techniques can be used to facilitate geoscientific data mining and analysis. Some special requirements of large scale geoscientific data mining that are addressed include geoscientific data modeling, parallel query processing, and heterogeneous distributed data access. Introduction A tremendous amount of raw spatio-temporal data is generated as a result of various observations, experiments, and model simulations. For example, NASA EOS expects to produce over 1 TByte of raw data and scientific data products per day by the year 2000, and a 100-year UCLA AGCM simulation (Mechoso et al. 1991) running at a resolution of 1 ffi \\Theta1:25...