TY - GEN
T1 - S2CE
T2 - 15th ACM International Conference on Distributed and Event-Based Systems, DEBS 2021
AU - Kourtellis, Nicolas
AU - Herodotou, Herodotos
AU - Grzenda, MacIej
AU - Wawrzyniak, Piotr
AU - Bifet, Albert
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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 position paper proposes Stream to Cloud and 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.
AB - 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 position paper proposes Stream to Cloud and 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.
KW - cloud analytics
KW - data stream analysis
KW - edge analytics
KW - machine and deep learning
KW - stream mining
U2 - 10.1145/3465480.3466926
DO - 10.1145/3465480.3466926
M3 - Conference contribution
AN - SCOPUS:85110291029
T3 - DEBS 2021 - Proceedings of the 15th ACM International Conference on Distributed and Event-Based Systems
SP - 103
EP - 113
BT - DEBS 2021 - Proceedings of the 15th ACM International Conference on Distributed and Event-Based Systems
A2 - Margara, Alessandro
A2 - Della Valle, Emanuele
A2 - Artikis, Alexander
A2 - Tatbul, Nesime
A2 - Parzyjegla, Helge
PB - Association for Computing Machinery, Inc
Y2 - 28 June 2021 through 2 July 2021
ER -