• JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
 
  Bookmark and Share
 
 
Master's Dissertation
DOI
10.11606/D.18.2019.tde-19022019-134228
Document
Author
Full name
Jonas Rossi Dourado
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2018
Supervisor
Committee
Maciel, Carlos Dias (President)
Delbem, Alexandre Cláudio Botazzo
Shinoda, Ailton Akira
Title in English
Delayed Transfer Entropy applied to Big Data
Keywords in English
Big Data analysis
Causality
Delayed Transfer Entropy
Heterogeneous computer cluster
Parallelism strategies
Surrogate
Abstract in English
Recent popularization of technologies such as Smartphones, Wearables, Internet of Things, Social Networks and Video streaming increased data creation. Dealing with extensive data sets led the creation of term big data, often defined as when data volume, acquisition rate or representation demands nontraditional approaches to data analysis or requires horizontal scaling for data processing. Analysis is the most important Big Data phase, where it has the objective of extracting meaningful and often hidden information. One example of Big Data hidden information is causality, which can be inferred with Delayed Transfer Entropy (DTE). Despite DTE wide applicability, it has a high demanding processing power which is aggravated with large datasets as those found in big data. This research optimized DTE performance and modified existing code to enable DTE execution on a computer cluster. With big data trend in sight, this results may enable bigger datasets analysis or better statistical evidence.
Title in Portuguese
Delayed Transfer Entropy aplicado a Big Data
Keywords in Portuguese
Análise de Big Data
Causalidade
Cluster heterogêneo de computadores
Delayed Transfer Entropy
Estratégias de paralelismo
Surrogate
Abstract in Portuguese
A recente popularização de tecnologias como Smartphones, Wearables, Internet das Coisas, Redes Sociais e streaming de Video aumentou a criação de dados. A manipulação de grande quantidade de dados levou a criação do termo Big Data, muitas vezes definido como quando o volume, a taxa de aquisição ou a representação dos dados demanda abordagens não tradicionais para analisar ou requer uma escala horizontal para o processamento de dados. A análise é a etapa de Big Data mais importante, tendo como objetivo extrair informações relevantes e às vezes escondidas. Um exemplo de informação escondida é a causalidade, que pode ser inferida utilizando Delayed Transfer Entropy (DTE). Apesar do DTE ter uma grande aplicabilidade, ele possui uma grande demanda computacional, esta última, é agravada devido a grandes bases de dados como as encontradas em Big Data. Essa pesquisa otimizou e modificou o código existente para permitir a execução de DTE em um cluster de computadores. Com a tendência de Big Data em vista, esse resultado pode permitir bancos de dados maiores ou melhores evidências estatísticas.
 
WARNING - Viewing this document is conditioned on your acceptance of the following terms of use:
This document is only for private use for research and teaching activities. Reproduction for commercial use is forbidden. This rights cover the whole data about this document as well as its contents. Any uses or copies of this document in whole or in part must include the author's name.
Jonas.pdf (5.17 Mbytes)
Publishing Date
2019-03-15
 
WARNING: Learn what derived works are clicking here.
All rights of the thesis/dissertation are from the authors
CeTI-SC/STI
Digital Library of Theses and Dissertations of USP. Copyright © 2001-2021. All rights reserved.