Gmavis : a domain-specific language for large-scale geospatial data visualization supporting multi-core parallelism

• Ano de defesa: 2016
• Autor(a) principal: Ledur, Cleverson Lopes
• Orientador(a): Fernandes, Luiz Gustavo Leão
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
• Departamento: Faculdade de Informática
• País: Brasil
• Palavras-chave em Português: LINGUAGEM DE PROGRAMAÇÃO DE DOMÍNIO ESPECÍFICO (COMPUTADORES); PROCESSAMENTO PARALELO; INFORMÁTICA
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/6837

Data generation has increased exponentially in recent years due to the popularization of technology. At the same time, information visualization enables the extraction of knowledge and useful information through data representation with graphic elements. Moreover, a set of visualization techniques may help in information perception, enabling finding patterns and anomalies in data. Even tought it provides many benefits, the information visualization creation is a hard task for users with a low knowledge in computer programming. It becomes more difficult when these users have to deal with big data files since most tools do not provide features to abstract data preprocessing. In order to bridge this gap, we proposed GMaVis. It is a Domain-Specific Language (DSL) that offers a high-level description language for creating geospatial data visualizations through a parallel data preprocessor and a high-level description language. GMaVis was evaluated using two approaches. First we performed a programming effort analysis, using an analytical software to estimate development effort based on the code. This evaluation demonstrates a high gain in productivity when compared with programming effort required by other tools and libraries with similar purposes. Also, a performance evaluation was conducted in the parallel module that performs data preprocessing, which demonstrated a performance gain when compared with the sequential version.