Share Email Print
cover

Proceedings Paper

Challenges and solutions to mining Earth science data
Author(s): Rahul Ramachandran; Helen T. Conover; Sara J. Graves; Ken Keiser
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Data Mining has an enormous potential as a processing tool for Earth Science data. It provides a solution for extracting information from massive amounts of data. However, designing a data mining system for earth science applications is complex and challenging. The two key issues that need to be addressed in the design are (1) variability of data sets and (2) operations for extracting information. Data sets not only come in different formats, types and structures; they are also typically in different states of processing such as raw data, calibrated data, validated data, derived data or interpreted data. The mining system must be designed to be flexible to handle these variations in data sets. The operations needed in the mining system vary for different application areas within earth science. Operations could range from general-purpose operations such as image processing techniques or statistical analysis to highly specialized data set-specific science algorithms. The mining system should be extensible in its ability to process new data sets and add new operations without too much effort. The ADaM (Algorithm Development and Mining) system, developed at the Information Technology and Systems Center at the University of Alabama in Huntsville, is one such mining system designed with these capabilities. The system provides knowledge discovery, content-based searching and data mining capabilities for data values, as well as for metadata. It contains over 100 different operations, which can be performed on the input data stream.

Paper Details

Date Published: 6 April 2000
PDF: 6 pages
Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381740
Show Author Affiliations
Rahul Ramachandran, Univ. of Alabama in Huntsville (United States)
Helen T. Conover, Univ. of Alabama in Huntsville (United States)
Sara J. Graves, Univ. of Alabama in Huntsville (United States)
Ken Keiser, Univ. of Alabama in Huntsville (United States)


Published in SPIE Proceedings Vol. 4057:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology II
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top