2020/07/02 by Nicolas Kourtellis, Herodotos Herodotou, Kourtellis, Nicolas +7
Computer Science · #Cloud Computing and Resource Management #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #H.2.4 #Parallel #Time Series Analysis and Forecasting #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2007.01260
openalex publication_date 2020/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The explosive increase in volume, velocity, variety, and veracity of data generated by distributed and heterogeneous nodes such as IoT and other devices, continuously challenge the state of art in big data processing platforms and mining techniques. Consequently, it reveals an urgent need to address the ever-growing gap between this expected exascale data generation and the extraction of insights from these data. To address this need, this paper proposes Stream to Cloud & Edge (S2CE), a first of its kind, optimized, multi-cloud and edge orchestrator, easily configurable, scalable, and extensible. S2CE will enable machine and deep learning over voluminous and heterogeneous data streams running on hybrid cloud and edge settings, while offering the necessary functionalities for practical and scalable processing: data fusion and preprocessing, sampling and synthetic stream generation, cloud and edge smart resource management, and distributed processing.