Aplicação da arquitetura lambda na construção de um ambiente big data educacional para análise de dados
Tipo
Dissertação
Data de publicação
2017-02-09
Periódico
Citações (Scopus)
Autores
Mendes, Renê de Ávila
Orientador
Silva, Leandro Augusto da
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Lopes, Fábio Silva
Pimentel, Edson Pinheiro
Pimentel, Edson Pinheiro
Programa
Engenharia Elétrica
Resumo
To properly deal with volume, velocity and variety data dimensions in educational
contexts is a major concern for Educational Institutions and both Educational
Data Mining and Learning Analytics Researchers have cooperated to properly
address this challenge which is popularly called Big Data. Hardware developments
have been made to increase computing power, storage capacity and efficiency in
energy use. New technologies in databases, file systems and distributed systems,
as well as developments in data transmission techniques, data management, data
analysis and visualization have been trying to overcome the challenge of processing,
storing and analyzing large volumes of data and the inability to meet simultaneously
the requirements of consistency, availability and tolerance of partitions. Although
the architecture definition is the main task in a Big Data system design, objective
guidelines for the selection of the architecture and the tools for the implementation
of Big Data systems were not found in the literature. The present research aims to
analyze the main architectures for both batch and stream processing and to use one
of them in the construction of a Big Data environment, providing important orientations
to Researchers, Technicians and Managers. Academic data and logs of the
Virtual Learning Environment Moodle of an Academic Unit of a Higher Education
Institution are used.
Descrição
Palavras-chave
big data , educational data mining , arquitetura lambda , moodle
Assuntos Scopus
Citação
MENDES, Renê de Ávila. Aplicação da arquitetura lambda na construção de um ambiente big data educacional para análise de dados. 2017. 88 f. Dissertação( Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo.