An edge–cloud integrated framework for flexible and dynamic stream analytics-Reference-Cited by-同舟云学术

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An edge–cloud integrated framework for flexible and dynamic stream analytics

Published:2022-12 Issue: Volume:137 Page:323-335
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Wang Xin^ORCID,Khan Azim,Wang Jianwu^ORCID,Gangopadhyay Aryya,Busart Carl,Freeman Jade

Funder

U.S. Army

National Science Foundation

Army Research Laboratory

U.S. Department of Education

International Business Machines Corporation

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference45 articles.

1. Adaptive and efficient streaming time series forecasting with lambda architecture and spark;Pandya,2020

2. The TriLS approach for drift-aware time-series prediction in IIoT environment;Uchiteleva;IEEE Trans. Ind. Inf.,2021

3. Fog networking for machine health prognosis: A deep learning perspective;Qaisar,2017

4. Deepcham: Collaborative edge-mediated adaptive deep learning for mobile object recognition;Li,2016

5. An Introduction to Deepstream SDK;Purandare,2018

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