TY  - GEN
N2  - This book gathers a selection of peer-reviewed papers presented at the second Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020) conference, held in Shanghai, China, on 28-29 December 2020. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.
DO  - 10.1007/978-981-33-4572-0
DO  - doi
AB  - This book gathers a selection of peer-reviewed papers presented at the second Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020) conference, held in Shanghai, China, on 28-29 December 2020. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.
T1  - Big data analytics for cyber-physical system in smart city :BDCPS 2020, 28-29 December 2020, Shanghai, China /
AU  - Atiquzzaman, Mohammed,
AU  - Yen, Neil Y.,
AU  - Xu, Zheng,
VL  - v. 1303
CN  - QA76.9.B45
N1  - "Due to the COVID-19 outbreak problem, BDCPS 2020 conference will be held online by Tencent Meeting."
N1  - Includes author index.
ID  - 1433018
KW  - Big data
KW  - Cooperating objects (Computer systems)
KW  - Données volumineuses
KW  - Objets coopérants (Systèmes informatiques)
SN  - 9789813345720
SN  - 9813345721
TI  - Big data analytics for cyber-physical system in smart city :BDCPS 2020, 28-29 December 2020, Shanghai, China /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4572-0
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4572-0
ER  -