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School of Computing: Dissertations, Theses, and Student Research
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First Advisor
Peter Z. Revesz
Date of this Version
6-1-2006
Document Type
Dissertation
Citation
Jun Gao, Adaptive interpolation algorithms for temporal-oriented datasets, Proc. 13th International Symposium on Temporal Representation and Reasoning, Budapest, Hungary, IEEE Press, pp. 145-151, June 2006.
Abstract
Spatiotemporal datasets can be classified into two categories: temporal-oriented and spatial-oriented datasets depending on whether missing spatiotemporal values are closer to the values of its temporal or spatial neighbors. We present an adaptive spatiotemporal interpolation model that can estimate the missing values in both categories of spatiotemporal datasets. The key parameters of the adaptive spatiotemporal interpolation model can be adjusted based on experience.
Included in
Computer Engineering Commons, Databases and Information Systems Commons, Theory and Algorithms Commons
Comments
Copyright (c) 2006 Jun Gao, Ph.D. in Computer Science, University of Nebraska-Lincoln, December 2006. This work is part of her dissertation work and was supervised by Prof Peter Z. Revesz.