Dependable Deep Computation Model for Feature Learning on Big Data in Cyber-Physical Systems

2019 ◽  
Vol 3 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Qingchen Zhang ◽  
Laurence T. Yang ◽  
Zhikui Chen ◽  
Peng Li
Keyword(s):  
Big Data ◽  
Feature Learning ◽  
Cyber Physical Systems ◽  
Computation Model ◽  
Physical Systems

A Tensor-Train Deep Computation Model for Industry Informatics Big Data Feature Learning

2018 ◽  
Vol 14 (7) ◽  
pp. 3197-3204 ◽  
Author(s):  
Qingchen Zhang ◽  
Laurence T. Yang ◽  
Zhikui Chen ◽  
Peng Li
Keyword(s):  
Big Data ◽  
Feature Learning ◽  
Computation Model

Fog-Assisted Cloud Platforms for Big Data Analytics in Cyber Physical Systems

Smart Data ◽  
2019 ◽  
pp. 289-318 ◽  
Author(s):  
Md. Muzakkir Hussain ◽  
Mohammad Saad Alam ◽  
M.M. Sufyan Beg ◽  
S. M. Shariff
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

2020 ◽  
Vol 4 (3) ◽  
pp. 577-577
Author(s):  
Vania V Estrela
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Query Language ◽  
Treatment Decisions ◽  
Cyber Physical Systems ◽  
Detailed Knowledge ◽  
Data Handling ◽  
Search Query ◽  
Physical Systems ◽  
Database As A Service

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse. Methods: BCPSs attributes are presented and discussed.  One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs.  Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways.

scholarly journals Big Data for Cyber-Physical Systems

2020 ◽  
Vol 6 (4) ◽  
pp. 606-608
Author(s):  
Shiyan Hu ◽  
Xin Li ◽  
Haibo He ◽  
Shuguang Cui ◽  
Manish Parashar
Keyword(s):  
Big Data ◽  
Cyber Physical Systems ◽  
Physical Systems
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