Title :
Adaptive Interpolation Algorithms for Temporal-Oriented Datasets
Author_Institution :
Dept. of Comput. Sci. & Eng., Nebraska Univ., Lincoln, NE
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
Keywords :
interpolation; temporal databases; visual databases; adaptive spatiotemporal interpolation model; spatial-oriented datasets; spatiotemporal datasets; temporal-oriented datasets; Computer science; Data engineering; Data visualization; Interpolation; Nominations and elections; Pollution; Shape; Spatiotemporal phenomena; Temperature; Voting;
Conference_Titel :
Temporal Representation and Reasoning, 2006. TIME 2006. Thirteenth International Symposium on
Conference_Location :
Budapest
Print_ISBN :
0-7695-2617-9
DOI :
10.1109/TIME.2006.4