High resolution spatio-temporal datasets are being collected every day to record the behavior of several natural phenomena. However, data mining techniques are needed to extract relevant patterns from very large repositories and reveal spatial and temporal patterns in the behavior of these phenomena. To this aim, we propose a system for mining data with spatial and temporal characteristics, and for visualizing and interpreting the results. Within this system, we have developed two complementary 3D visualization environments, one based on Google Earth and one relying on a Java3D graphical user interface. In this paper, we illustrate the main features of the system we have developed and report on the main results we have obtained by analyzing the Hurricane Isabel dataset.
Towards a framework for mining and analyzing spatio-temporal datasets
FERRUCCI, Filomena;
2007-01-01
Abstract
High resolution spatio-temporal datasets are being collected every day to record the behavior of several natural phenomena. However, data mining techniques are needed to extract relevant patterns from very large repositories and reveal spatial and temporal patterns in the behavior of these phenomena. To this aim, we propose a system for mining data with spatial and temporal characteristics, and for visualizing and interpreting the results. Within this system, we have developed two complementary 3D visualization environments, one based on Google Earth and one relying on a Java3D graphical user interface. In this paper, we illustrate the main features of the system we have developed and report on the main results we have obtained by analyzing the Hurricane Isabel dataset.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
