@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 = "28 abr. 2024"
}