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@InProceedings{CâmaraAsRiFeLlVi:2016:BiEaOb,
               author = "C{\^a}mara, Gilberto and Assis, Luiz Fernando Ferreira Gomes and 
                         Ribeiro, Gilberto and Ferreira, Karine Reis and Llapa, Eduardo and 
                         Vinhas, L{\'u}bia",
          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)}",
                title = "Big earth observation data analytics",
            booktitle = "Proceedings...",
                 year = "2016",
                pages = "1",
         organization = "ACM SIGSPATIAL International Workshop, 5.",
             keywords = "Earth Observation, Array Databases, Big Data Analytics.",
             abstract = "Earth observation satellites produce petabytes of geospatial data. 
                         To manage large data sets, researchers need stable and efficient 
                         solutions that support their analytical tasks. Since the 
                         technology for big data handling is evolving rapidly, researchers 
                         find it hard to keep up with the new developments. To lower this 
                         burden, we argue that researchers should not have to convert their 
                         algorithms to specialised environments. Imposing a new API to 
                         researchers is counterproductive and slows down progress on big 
                         data analytics. This paper assesses the cost of 
                         research-friendliness, in a case where the researcher has 
                         developed an algorithm in the R language and wants to use the same 
                         code for big data analytics. We take an algorithm for remote 
                         sensing time series analysis on compare it use on map/reduce and 
                         on array database architectures. While the performance of the 
                         algorithm for big data sets is similar, organising image data for 
                         processing in Hadoop is more complicated and time-consuming than 
                         handling images in SciDB. Therefore, the combination of the array 
                         database SciDB and the R language offers an adequate support for 
                         researchers working on big Earth observation data analytics.",
  conference-location = "Burlingame, CA, USA",
      conference-year = "31 oct - 03 nov.",
                  doi = "10.1145/3006386.3006393",
                  url = "http://dx.doi.org/10.1145/3006386.3006393",
                 isbn = "9781450345811",
                label = "lattes: 6187040703676041 6 CamaraAsRiFeLlVi:2016:BiEaOb",
             language = "en",
           targetfile = "camara_big.pdf",
        urlaccessdate = "24 nov. 2020"
}


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