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Titlebook: Cloud Computing, Big Data & Emerging Topics; 8th Conference, JCC- Enzo Rucci,Marcelo Naiouf,Laura De Giusti Conference proceedings 2020 Spr

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书目名称Cloud Computing, Big Data & Emerging Topics
副标题8th Conference, JCC-
编辑Enzo Rucci,Marcelo Naiouf,Laura De Giusti
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Cloud Computing, Big Data & Emerging Topics; 8th Conference, JCC- Enzo Rucci,Marcelo Naiouf,Laura De Giusti Conference proceedings 2020 Spr
描述This book constitutes the revised selected papers of the 8th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2020, held in La Plata, Argentina*, in September 2020..The 11 full papers presented were carefully reviewed and selected from a total of 36 submissions. The papers are organized in topical sections of cloud computing and HPC; Big Data and machine and deep learning..*The conference was held virtually due to the COVID-19 pandemic..
出版日期Conference proceedings 2020
关键词artificial intelligence; big data; cloud computing; computer hardware; computer networks; computer system
版次1
doihttps://doi.org/10.1007/978-3-030-61218-4
isbn_softcover978-3-030-61217-7
isbn_ebook978-3-030-61218-4Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Hugo Grotius und die Sozialwissenschaften,topic. The objective of this work is to carry out a systematic literature review of cloud robotics for Industry 4.0, in an attempt to present the state of the art in this field and identify opportunities for future research. From the analysis of the results, we observe an emerging interest in this a
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Der Austauschbau im Großmaschinenbauusing a labeled subset of the VPHAS+ and 2MASS databases, with filters ., ., .,  H., and .. In order to avoid the reddening effect, we propose and evaluate the use of reddening-free . indices to enhance the generalization of the model to other databases and objects. To test the validity of the appro
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Data Management Optimization in a Real-Time Big Data Analysis System for Intensive Carein storage space use and delivery time of both real-time and historical data. Our results show that Kafka can be used for historical data storage. This in turn allows us to eliminate the NoSQL database of our previous system. Unlike other works, ours attempts to reduce the number of components to lo
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