Actas de congresos
Discovering Frequent Patterns On Agrometeorological Data With Triemotif
Registro en:
978-3-319-22348-3; 978-3-319-22347-6
Discovering Frequent Patterns On Agrometeorological Data With Triemotif. Springer-verlag Berlin, v. 227, p. 91-107 2015.
1865-1348
WOS:000367883300006
10.1007/978-3-319-22348-3_6
Autor
Chino
Daniel Y. T.; Goncalves
Renata R. V.; Romani
Luciana A. S.; Traina
Caetano
Jr.; Traina
Agma J. M.
Institución
Resumen
The "food safety" issue has concerned governments from several countries. The accurate monitoring of agriculture have become important specially due to climate change impacts. In this context, the development of new technologies for monitoring are crucial. Finding previously unknown patterns that frequently occur on time series, known as motifs, is a core task to mine the collected data. In this work we present a method that allows a fast and accurate time series motif discovery. From the experiments we can see that our approach is able to efficiently find motifs even when the size of the time series goes longer. We also evaluated our method using real data time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area, which are really useful for data analysis. 227
91 107