Machine Learning Platform for Extreme Scale Computing on Compressed IoT Data

Seshu Tirupathi, Dhaval Salwala, Giulio Zizzo, Ambrish Rawat, Mark Purcell, Søren 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

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)


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 predictions are also steadily increasing. In this paper, we provide an overview of a novel end-to-end platform that provides efficient ingestion, compression, transfer, query processing, and machine learning-based analytics for high-frequency and high-volume time series from IoT. The performance of the platform is evaluated using real-world dataset from RES installations. The results show the importance of high-frequency analytics and the surprisingly positive impact of error bounded lossy compression on machine learning in the form of AutoML. For example, when detecting yaw misalignments in wind turbines, an improvement of 9% in accuracy was observed for AutoML models on lossy compressed data compared to the current industry standard of 10-minute aggregated data. Thus, these small-scale experiments show the potential of the platform, and larger pilots are planned.
Titel2022 IEEE International Conference on Big Data (Big Data)
RedaktørerShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Antal sider7
ForlagIEEE Communications Society
Publikationsdato20 dec. 2022
ISBN (Trykt)978-1-6654-8046-8
ISBN (Elektronisk)9781665480451
StatusUdgivet - 20 dec. 2022
Begivenhed2022 IEEE International Conference on Big Data (Big Data) - Osaka, Japan
Varighed: 17 dec. 202220 dec. 2022


Konference2022 IEEE International Conference on Big Data (Big Data)
LokationOsaka, Japan


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