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Doctoral Thesis
DOI
https://doi.org/10.11606/T.45.2014.tde-10112014-110134
Document
Author
Full name
Pedro Losco Takecian
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2014
Supervisor
Committee
Ferreira, João Eduardo (President)
Italiano, Isabel Cristina
Oikawa, Márcio Katsumi
Salgado, Ana Carolina Brandão
Wassermann, Renata
Title in Portuguese
Diretrizes metodológicas e validação estatística de dados para a construção de data warehouses
Keywords in Portuguese
análise de dados
aprendizado de máquina
arquitetura modular
data warehouse
modelagem conceitual
validação de dados
Abstract in Portuguese
Os sistemas de integração de dados que usam a arquitetura de data warehouse (DW) têm se tornado cada vez maiores e mais difíceis de gerenciar devido à crescente heterogeneidade das fontes de dados envolvidas. Apesar dos avanços tecnológicos e científicos, os projetos de DW ainda são muito lentos na geração de resultados pragmáticos. Este trabalho busca responder à seguinte questão: como pode ser reduzida a complexidade do desenvolvimento de sistemas de DW que integram dados provenientes de sistemas transacionais heterogêneos? Para isso, apresenta duas contribuições: 1) A criação de diretrizes metodológicas baseadas em ciclos de modelagem conceitual e análise de dados para guiar a construção de um sistema modular de integração de dados. Essas diretrizes foram fundamentais para reduzir a complexidade do desenvolvimento do projeto internacional Retrovirus Epidemiology Donor Study-II (REDS-II), se mostrando adequadas para serem aplicadas em sistemas reais. 2) O desenvolvimento de um método de validação de lotes de dados candidatos a serem incorporados a um sistema integrador, que toma decisões baseado no perfil estatístico desses lotes, e de um projeto de sistema que viabiliza o uso desse método no contexto de sistemas de DW.
Title in English
Methodological guidelines and statistical data validation for the construction of data warehouses
Keywords in English
conceptual modeling
data analysis
data validation
data warehouse
machine learning
modular architecture
Abstract in English
Data integration systems that use data warehouse (DW) architecture are becoming bigger and more difficult to manage due to the growing heterogeneity of data sources. Despite the significant advances in research and technologies, many integration projects are still too slow to generate pragmatic results. This work addresses the following question: how can the complexity of DW development for integration of heterogeneous transactional information systems be reduced? For this purpose, we present two contributions: 1) The establishment of methodological guidelines based on cycles of conceptual modeling and data analysis to drive construction of a modular data integration system. These guidelines were fundamental for reducing the development complexity of the international project Retrovirus Epidemiology Donor Study-II (REDS-II), proving suited to be applied in real systems. 2) The development of a validation method of data batches that are candidates to be incorporated into an integration system, which makes decisions based on the statistical profile of these batches, and a project of a system that enables the use of this method in DW systems context.
 
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tese.pdf (1.98 Mbytes)
Publishing Date
2014-11-13
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • Takecian, Pedro L., et al. Methodological guidelines for reducing the complexity of data warehouse development for transactional blood bank systems [doi:10.1016/j.dss.2013.02.008]. Decision Support Systems [online], 2013, vol. 55, p. 728-739.
  • De Almeida-Neto, Cesar, et al. Demographic characteristics and prevalence of serologic markers among blood donors who use confidential unit exclusion (CUE) in São Paulo, Brazil: implications for modification of CUE polices in Brazil [doi:10.1111/j.1537-2995.2010.02799.x]. Transfusion (Arlington, Va.) [online], 2011, vol. 51, p. 191-197.
  • TAKECIAN, P. L., and FERREIRA, J. E. Methodological Guidelines and Adaptive Statistical Data Validation to Build Effective DataWarehouses. In Simpósio Brasileiro de Bancos de Dados, São Paulo, 2012. Proceedings of the 27th Brazilian Symposium on Databases - Thesis and Dissertation Workshop.Sâo Paulo : Sociedade Brasileira de Computação, 2012. Resumo. Disponível em: http://data.ime.usp.br/sbbd2012/artigos/pdfs/sbbd_wtd_10.pdf.
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