Authors:
Claudinei Garcia de Andrade
;
Marcela Xavier Ribeiro
;
Cristiane Yaguinuma
and
Marilde Terezinha Prado Santos
Affiliation:
Federal University of São Carlos, Brazil
Keyword(s):
Time Series, Similarity Search, Coulomb’s Law.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Data Mining
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Large Scale Databases
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Several areas of knowledge use systematic and controlled observation, obtained from measurements taken at regular intervals, as a tool for behavioral analysis of phenomena, such as meteorology, which uses the observations to predict the climate behavior. Furthermore, with the advance of technology, the instruments used to measure observations have grown dramatically and the amount of data available for analysis has become greater than the ability to analyze them. In this context, this paper aims to propose a method, based on the principle of Coulomb's Law, for similarity search in time series and thus discovering intrinsic knowledge from these data. Experimental results conducted on climatic data of Brazilian cities and the sea surface temperature showed that the proposed method outperforms traditional methods on performance and accuracy and it is promising for finding similarity in series.