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@InProceedings{AssisRGVLSMC:2016:BiDaSt,
               author = "Assis, Luiz Fernando Ferreira Gomes and Ribeiro, Gilberto and 
                         Gomes, Karine Reis Ferreira and Vinhas, L{\'u}bia and Llapa, 
                         Eduardo and Sanchez, Alber and Maus, Victor Wegner and 
                         C{\^a}mara, Gilberto",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Big data streaming for remote sensing time series analytics using 
                         MapReduce",
            booktitle = "Anais...",
                 year = "2016",
         organization = "Brazilian Symposium on GeoInformatics, 17. (GEOINFO)",
             abstract = "Governmental agencies provide a large and open set of satellite 
                         imagery which can be used to track changes in geographic features 
                         over time. The current available analysis methods are complex and 
                         they are very demanding in terms of computing capabilities. Hence, 
                         scientist cannot reproduce analytic results because of lack of 
                         computing infrastructure. Therefore, we propose a combination of 
                         streaming and map-reduce for time series analysis of time series 
                         data. We tested our proposal by applying the classification 
                         algorithm BFAST to MODIS imagery. Then, we evaluated account 
                         computing performance and requirements quality attributes. Our 
                         results revealed that the combination between Hadoop and R can 
                         handle complex analysis of remote sensing time series.",
  conference-location = "Campos do Jord{\~a}o, SP",
      conference-year = "27-30 nov. 2016",
             language = "en",
           targetfile = "229-239assis-1.pdf",
        urlaccessdate = "28 nov. 2020"
}


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