Dstream: Plataforma de Coleta, Processamento e Armazenamento de Streams de Dados de Sensoriamento Remoto
Abstract
Currently, one of the best applications has been in identifying trans-formations on Earth surface, which is changing at an unprecedented rate. Hence, great efforts have been made to create solutions to identify these changes by processing large volumes of data (Big Data) continuously generated by several sources (streaming). The goal of this paper is to explore the parallel architectures to proposes a new distributed solution for the collect, storing, in-dexing and processing of time series of remote sensing images.
Keywords:
Google Cloud, Amazon Web Service Cloud, Workload, MongoDB, Preemptable Machines.
References
Chen, M., Mao, S., and Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2):171–209.
Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., and Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104(11):2207–2219.
de Oliveira, S. S. T., de Castro Cardoso, M., dos Santos, W., Costa, P., do Sacramento Ro- processing in a distributed computing environment. Revista Brasileira de Cartografia, 69(5).
Fan, J., Han, F., and Liu, H. (2014). Challenges of big data analysis. National science review, 1(2):293–314.
Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., and Jie, W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51:47–60.
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., and Keogh, E. (2013). Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(3):10.
Sottile, M. J., Mattson, T. G., and Rasmussen, C. E. (2009). Introduction to concurrency in programming languages. CRC Press.
Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., and Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104(11):2207–2219.
de Oliveira, S. S. T., de Castro Cardoso, M., dos Santos, W., Costa, P., do Sacramento Ro- processing in a distributed computing environment. Revista Brasileira de Cartografia, 69(5).
Fan, J., Han, F., and Liu, H. (2014). Challenges of big data analysis. National science review, 1(2):293–314.
Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., and Jie, W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51:47–60.
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., and Keogh, E. (2013). Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data (TKDD), 7(3):10.
Sottile, M. J., Mattson, T. G., and Rasmussen, C. E. (2009). Introduction to concurrency in programming languages. CRC Press.
Published
2018-08-08
How to Cite
OLIVEIRA, Sávio; FERREIRA, Laerte; SANTOS, Claudinei; MARTINS, Wellington; RODRIGUES, Vagner.
Dstream: Plataforma de Coleta, Processamento e Armazenamento de Streams de Dados de Sensoriamento Remoto. In: REGIONAL SCHOOL ON INFORMATICS OF GOIÁS (ERI-GO), 2018. , 2018, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 331-336.
