DocumentCode
169987
Title
Mining DEOPS Records: Big Data´s Insights into Dictatorship
Author
De Moraes Navarro, D. ; Prati, R.C.
Author_Institution
Centro de Mat., Comput. e Cognicao (CMCC), Univ. Fed. do ABC (UFABC), Santo Andre, Brazil
Volume
2
fYear
2014
fDate
20-24 Oct. 2014
Firstpage
67
Lastpage
70
Abstract
Historical data provide valuable information for the nderstanding of human interactions through time. However, mining this data is challenging as the available records are generally noise digitized handwritten, typewritten or press printed documents. In this research proposal, we plan to develop tools and techniques for pre-processing and extracting information from documents of the military dictatorship period that ruled Brazil from 1964 to 1985. The data to be analyzed consists of digitized images of records from DEOPS/SP (São Paulo State Department of Political and Social Order), an emblematic police agency which have monitored (and in some cases, harassed and tortured) hundreds of thousands Brazilian citizens during that period. The idea is to use state-of-the-art powerful artificial intelligence algorithms in conjunction with crowd sourcing techniques to pre-process and extract information from this important period of the Brazilian History.
Keywords
Big Data; artificial intelligence; data analysis; data mining; document handling; history; military computing; police; Brazilian history; DEOPS record mining; DEOPS-SP; São Paulo State Department of Political and Social Order; artificial intelligence algorithms; big data; crowd sourcing techniques; data analysis; data mining; document information extraction; document preprocessing; emblematic police agency; historical data; military dictatorship; noise digitized handwritten documents; press printed documents; typewritten documents; Character recognition; Crowdsourcing; Data mining; Optical character recognition software; Presses; Smart phones; Strips; Brazilian military dictatorship; DEOPS; artificial intelligence; big data; crowdsourcing; data mining; escience; image processing; machine learning; text processing;
fLanguage
English
Publisher
ieee
Conference_Titel
e-Science (e-Science), 2014 IEEE 10th International Conference on
Conference_Location
Sao Paulo
Print_ISBN
978-1-4799-4288-6
Type
conf
DOI
10.1109/eScience.2014.34
Filename
6972099
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