EVENTO
Desvendando Objetos em Grandes Volumes de Dados
Tipo de evento: Defesa de Dissertação de Mestrado
Big data processing is expected to empower decision-making as more information becomes accessible to analytical tools. In this work, we argue that the data deluge produced by the Big Data phenomenon blurs, amongst billions of dataset elements, high-level objects that can only be perceived once adequate composition models are in place. We argue that identifying such objects is relevant for various disciplines and we provide an example in astronomy. This work formulates the problem of Unveiling Objects in Big Data (UOBD) and presents strategies to compose Objects of Interest from elements in large datasets. We present a novel technique with some pruning strategies to find these objects while we show the robustness and efficiency of our technique using a SPARK implementation over Hadoop parallel architecture.
Data Início: 29/02/2016 Hora: 14:00 Data Fim: 29/02/2016 Hora: 17:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio B
Aluno: Amir Hassan Khatibi Moghadam - LNCC -
Orientador: Eduardo Ogasawara - CEFET - Fabio Andre Machado Porto - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Artur Ziviani - Laboratório Nacional de Computação Científica - LNCC Fabio Andre Machado Porto - Laboratório Nacional de Computação Científica - LNCC Hélio Cortes Vieira Lopes - -
Suplente Banca Examinadora: Luiz Manoel Rocha Gadelha Júnior - German Cancer Research Center - DKFZ Marco Antonio Casanova - Pontifícia Universidade Católica do Rio de Janeiro - PUC-RIO
