Data/hora: 14/05/2024 05:58:58

Biblioteca(s): 	Embrapa Agricultura Digital.	
Tipo da produção científica: 	Artigo em Anais de Congresso / Nota Técnica	
Data corrente: 	25/05/2009	
Data da última atualização: 	15/01/2020	
Autoria: 	ROMANI, L. A. S.; SOUSA, E. P. M. de; RIBEIRO, M. X.; ZULLO JÚNIOR. J.; TRAINA JÚNIOR, C.; TRAINA, A. J. M.	
Afiliação: 	LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, ICMC/USP; MARCELA X. RIBEIRO, ICMC/USP; JURANDIR ZULLO JÚNIOR, CEPAGRI/UNICAMP; CAETANO TRAINA JÚNIOR, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP.	
Título: 	Employing fractal dimension to analyze climate and remote sensing data streams.	
Ano de publicação: 	2009	
Fonte/Imprenta: 	In: SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 9., 2009, Sparks. Proceedings... Society for Industrial and Applied Mathematics, Philadelphia, 2009.	
Páginas: 	Não paginado.	
Idioma: 	Inglês	
Notas: 	SDM 2009.	
Conteúdo: 	Recently, huge amounts of climate data and remote sensing images have been stored by several institutions around the world. Improvements in the data gathering, aiming at making it available to the domain specialist the information needed for decision making. Also, data usually change their behavior over time in climate and remote sensing areas, evolving according to agrometeorological aspects. In this work, we propose a framework to monitor evolving climate and remote sensing data by emplooying a fast and low-cost process based on the fractal dimension extracted from the collected data. Significant changes in data trends are captured by the fractal-based monitoring process. The changes are evaluated by employing a statistical test to compare the data in consecutive time periods, revealing which data attributes are responsible for the trend changes and how they influence them. Therefore, the proposed framework monitors and reveals important variations on the climate that should be considered to make the agribusiness of the remote sense regions more productive. In particular, we show that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive.	
Palavras-Chave: 	Dados multimídia; Data streams; Dimensão fractal; Inteligência artificial; Multimedia data; Teoria dos fractais.	
Thesagro: 	Agricultura; Clima; Sensoriamento remoto.	
Thesaurus Nal: 	Climate; Fractal dimensions; Remote sensing.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (12934-1) - UPC - AA - PP -  -  - 2009.00063 - DES
CNPTIA - (12934-2) - UPC - AA - DD -  -  -  - DIS