01359nam a2200193 a 450000100080000000500110000800800410001902000220006002200140008210000170009624501080011326001020022130000140032352007440033765300130108165300140109465300370110870000200114521438872024-01-23       2022    bl uuuu     u00u1 u    #d  a978-1-6654-3418-8  a2325-65161 aALVES, G. M.  aTomographic image reconstruction method using mapreduce in big data environment.h[electronic resource]  aIn: IEEE International Conference on Semantic Computing (ICSC), 16th, Laguna Hills, CA, USAc2022  a293 - 298  aIn this study, we propose a new tomographic reconstruction method for high-resolution samples based on the MapReduce model. We executed the method in a big data environment with a cluster installed on the Amazon Web Services (AWS) platform. The big data environment framework considered four sets of matrices from a single heterogeneous plexiglass phantom sample, totaling 7,840 matrices (35.63 GB) processed by 12 different frameworks and producing 427.56 GB of processed tomographic data. The proposed method enabled the analysis of large numbers of agricultural samples using X-ray tomography to support management based on precision agriculture paradigms,the decision-making processes of which require an increasing number of analyses.  aBig data  aMapReduce  aTomographic image reconstruction1 aCRUVINEL, P. E.