Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data

Autor: Seshu Tirupathi, Dhaval Salwala, Giulio Zizzo, Ambrish Rawat, Mark Purcell, Soren Kejser Jensen, Christian Thomsen, Nguyen Ho, Carlos E. Muniz Cuza, Jonas Brusokas, Torben Bach Pedersen, Giorgos Alexiou, Giorgos Giannopoulos, Panagiotis Gidarakos, Alexandros Kalimeris, Stavros Maroulis, George Papastefanatos, Ioannis Psarros, Vassilis Stamatopoulos, Manolis Terrovitis

Publikováno v: Tirupathi, S, Salwala, D, Zizzo, G, Rawat, A, Purcell, M, Jensen, S K, Thomsen, C, Ho, N, Cuza, C E M, Brusokas, J, Pedersen, T B, Alexiou, G, Giannopoulos, G, Gidarakos, P, Kalimeris, A, Maroulis, S, Papastefanatos, G, Psarros, I, Stamatopoulos, V & Terrovitis, M 2022, Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data . in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), 2022 IEEE International Conference on Big Data (Big Data) ., 10020540, IEEE Communications Society, pp. 3179-3185, 2022 IEEE International Conference on Big Data (Big Data), 17/12/2022 . https://doi.org/10.1109/BigData55660.2022.10020540

With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated and analyzed, especially in IoT domains like energy and smart homes. Consequently, applications that require accurate short-term forecasts and pre

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