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@InCollection{RamosTaAlAcCuDi:2016:DiSyPe,
               author = "Ramos, Marcelo Paiva and Tasinaffo, Paulo Marcelo and Almeida, 
                         Eug{\^e}nio Sper de and Achite, Luis Marcelo and Cunha, Adilson 
                         Marques da and Dias, Luiz Alberto Vieira",
                title = "Distributed systems performance for big data",
            booktitle = "Information technology: new generations",
            publisher = "Springer Verlag",
                 year = "2016",
               editor = "Lafiti, Shahram",
                pages = "733--744",
             keywords = "Big data, Climate prediction, Cluster HPC, Distributed systems, 
                         Hadoop, Hive, Python.",
             abstract = "This paper describes a methodology for working with distributed 
                         systems, and achieve performance in Big Data, through the 
                         framework Hadoop, Python programming language, and Apache Hive 
                         module. The efficiency of the proposed methodology is tested 
                         through a case study that addresses a real problem found in the 
                         supercomputing environment of the Center for Weather Forecasting 
                         and Climate Studies linked to the Brazilian Institute for Space 
                         Research (CPTEC/INPE), which provides Society a work able to 
                         predict disasters and save people lives. In all three experiments 
                         involving the issue, using the Cray XT-6 supercomputer: (i) the 
                         first issue involves programming in Python and a sequential and 
                         monoprocessed arquitecture; (ii) the second uses Python and Hadoop 
                         framework, over parallel and distributed arquitecture; (iii) the 
                         latter combines Hadoop and Hive in a parallel and distributed 
                         arquitecture. The main results of these experiments are compared, 
                         discussed, and topics beyond the scope in this research are 
                         exposed as recommendations and suggestions for future work.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)}",
                  doi = "10.1007/978-3-319-32467-8_64",
                  url = "http://dx.doi.org/10.1007/978-3-319-32467-8_64",
                 isbn = "978-331932466-1",
             language = "en",
         serieseditor = "Kacprzyk, Janusz",
          seriestitle = "Advances in Intelligent Systems and Computing",
           targetfile = "ITNG1_DistributedSystemsPerformanceForBigData.pdf",
               volume = "448",
        urlaccessdate = "26 nov. 2020"
}


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