2018 · 42 citations · 8 references
Cluster ComputingEngineeringMap-reduceDistributed Data AnalyticsBig RemoteData ScienceData-intensive PlatformManagementContinuous GenerationData IntegrationParallel ComputingData ManagementHadoop MapreduceFile SystemsPlatforms HadoopComputer ScienceData ProcessingCloud ComputingRemote SensingMassive Data ProcessingBig Data
The continuous generation of huge amount of remote sensing (RS) data is becoming a challenging task for researchers due to the 4 Vs characterizing this type of data (volume, variety, velocity and veracity). Many platforms have been proposed to deal with big data in RS field. This paper focus on the comparison of two well-known platforms of big RS data namely Hadoop and Spark. We start by describing the two platforms Hadoop and Spark. The first platform is designed for processing enormous unstructured data in a distributed computing environment. It is composed of two basic elements : 1) Hadoop Distributed file system for storage, and 2) Mapreduce and Yarn for parallel processing, scheduling the jobs and analyzing big RS data. The second platform, Spark, is composed by a set of libraries and uses the resilient distributed data set to overcome the computational complexity. The last part of this paper is devoted to a comparison between the two platforms.
8
Big Data technologies: A survey
Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen et al. · Journal of King Saud University - Computer and Information Sciences · 2017 · 951 citations · Full text
Remote sensing big data computing: Challenges and opportunities
Yan Ma, Haiping Wu, Lizhe Wang et al. · Future Generation Computer Systems · 2014 · 630 citations