@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",
ibi = "8JMKD3MGP3W34P/3N2U8K2",
url = "http://urlib.net/ibi/8JMKD3MGP3W34P/3N2U8K2",
targetfile = "229-239assis-1.pdf",
urlaccessdate = "19 abr. 2024"
}