@InProceedings{HappCostFeit:2017:CoMaTe,
               author = "Happ, Patrick Nigri and Costa, Gilson A O P and Feitosa, Raul 
                         Queiroz",
                title = "Uma compara{\c{c}}{\~a}o entre MapReduce e Tez para 
                         segmenta{\c{c}}{\~a}o de imagens em ambientes de 
                         computa{\c{c}}{\~a}o em nuvem",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7938--7945",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Driven mainly by the modern advances in the Earth Observation 
                         technology in the last years, the increase of the remote sensing 
                         data volume represents a new challenge. The current available 
                         image processing solutions fail to deliver the expected 
                         performance and scalability required to deal with this large 
                         volume of data. Aiming to face this problem, the authors proposed, 
                         in a recent work, a distributed strategy for region growing 
                         segmentation of arbitrarily large images. The presented strategy 
                         is able to perform in cloud-computing environments and most of the 
                         distributed architectures. The original implementation is based on 
                         the MapReduce model, which offers a highly scalable and reliable 
                         framework for storing and processing massive data in cloud 
                         computing environments. However, MapReduce is losing popularity 
                         lately and it is being slowly replaced by different engines that 
                         have been emerged. Since the distributed image segmentation is a 
                         method independent from its implementation, this paper aim to 
                         compare the original implementation using MapReduce to a new 
                         implementation using a different distributed framework. In this 
                         work, the new implementation is based on Apache Tez. Tez enhances 
                         the MapReduce paradigm by improving its speed while maintaining 
                         MapReduce''s ability to scale to petabytes of data. The 
                         experiments carried out on a virtual cluster in a commercial 
                         cloud-computing infrastructure demonstrated that both 
                         implementations present a potential scalable and efficient 
                         solution, with Tez achieving a better performance.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60164",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMGM5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMGM5",
           targetfile = "60164.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}