Affiliation(s)
Universidade Católica do SalvadorandCompanhia de Eletricidade do Estado da Bahia, Brazil
ABSTRACT
This paper presents a study
of optimization of operational recovery credit default with geoprocessing use
through geoprocessing tools, developed in the Receivables
Management Companhia de Eletricidade do Estado da Bahia-COELBA sector. The work
was initially based on the application of Data Mining Tools for Software KNIME
2.9 and later use of the tool of GIS-ArcGIS 10.X/ESRI .Were evaluated and
applied analytical processing algorithms, to improve the process of spatial
selection and define the best sets logistical credit recovery. The focus study,
based on geoprocessing use in cutting action is due to the fact that this
process has the largest collection efficiency. It is understood that the efficiency
of the cutting action, should the great importance that electricity has on
modern life. The research was guided its evolution from analysis of algorithms
agglutination and georeferenced database, whose focus was and is acting in the
cutting action due to the fact that this process has the largest collection
efficiency. For the implementation of the study, through some geoprocessing
techniques, the Mpar-cluster, this optimization model was developed from a
custom algorithm for spatial selection, that had with subsidy: information
stored in geographic databases, database information alphanumeric, images
(aerial photographs and satellite images), text files, and digital tables.
KEYWORDS
Geoprocessing, recovery
credit, mpar-cluster.
Cite this paper
References
[1] Agência
Nacional de Energia Elétrica – ANEEL, Resolução nº 414, Disponível em: http://www.aneel.gov.br/biblioteca/downloads/livros/REN_414_2010_atual_REN_499_2012.pdf,
Accessed March 01, 2014.
[2] Maslow,
A. 1962. Introdução à psicologia do ser. Rio de Janeiro: Eldorado.
[3] Jain,
A. K., Murthy, M. N., and Flynn, P. J. 1999. “Data Clustering: A Review.” ACM
Computing Survey 31 (3): 264-323.
[4] Oliveira,
D. De P. R. 2005. Planejamento estratégico – conceitos, metodologia, práticas
(22nd ed.). São Paulo: Atlas.
[5] Davis,
C., and Câmara, G. 2014. “Arquitetura de Sistemas de Informações Geográficas.”
Accessed: http://www.dpi.inpe.br/gilberto/livro/introd/cap3-arquitetura.pdf,
July 01.
Câmara, G., and Monteiro, A. M. V. 2014. “Conceitos
Básicos em Ciência da Geoinformação.”
http://www.dpi.inpe.br/gilberto/livro/introd/cap2-conceitos.pdf, August 15.
XML 2831 Views