scholarly journals Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Engineering ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 662-670 ◽  
Author(s):  
Junliang Wang ◽  
Peng Zheng ◽  
Youlong Lv ◽  
Jingsong Bao ◽  
Jie Zhang
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Manufacturing Systems ◽  
Fog Computing ◽  
Industrial Big Data

scholarly journals Big Data Integration Method of Mathematical Modeling and Manufacturing System Based on Fog Calculation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Chen
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Integration ◽  
Manufacturing System ◽  
Fog Computing ◽  
Relevant Literature ◽  
Fast Response ◽  
Computing Methods ◽  
Computer Algorithms ◽  
Huge Impact

Using big data to promote economic development, improve social governance, and improve service and regulatory capabilities is becoming a trend. However, the current cloud computing for data processing has been difficult to meet the demand, and the server pressure has increased dramatically, so people pay special attention to the big data integration of fog computing. In order to make the application of big data meet people’s needs, we have established relevant mathematical models based on fog calculation, made system big data integration, collected relevant data, designed experiments, and obtained relevant research data by reviewing relevant literature and interviewing professionals. The research shows that big data integration using fog computing modeling has the characteristics of fast response and stable function. Compared with cloud computing and previous computer algorithms, big data integration has obvious advantages, and the computing speed is nearly 20% faster than cloud computing and about 35% higher than other computing methods. This shows that big data integration built by fog computing can have a huge impact on people’s lives.

A Unified Framework and Platform for Designing of Cloud-Based Machine Health Monitoring and Manufacturing Systems

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Shanhu Yang ◽  
Behrad Bagheri ◽  
Hung-An Kao ◽  
Jay Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Working Conditions ◽  
Health Monitoring ◽  
Manufacturing Systems ◽  
Heterogeneous Data ◽  
Service Industries ◽  
Industrial Big Data ◽  
Machine Health Monitoring ◽  
Machine Health

Cloud computing has brought about new service models and research opportunities in the manufacturing and service industries with advantages in ubiquitous accessibility, convenient scalability, and mobility. With the emerging industrial big data prompted by the advent of the internet of things and the wide implementation of sensor networks, the cloud computing paradigm can be utilized as a hosting platform for autonomous data mining and cognitive learning algorithms. For machine health monitoring and prognostics, we investigate the challenges imposed by industrial big data such as heterogeneous data format and complex machine working conditions and further propose a systematically designed framework as a guideline for implementing cloud-based machine health prognostics. Specifically, to ensure the effectiveness and adaptability of the cloud platform for machines under complex working conditions, two key design methodologies are presented which include the standardized feature extraction scheme and an adaptive prognostics algorithm. The proposed strategy is further demonstrated using a case study of machining processes.

Evaluation Model for Big Data Integration Tools

2019 ◽  
pp. 601-610 ◽  
Author(s):  
Ângela Alpoim ◽  
Tiago Guimarães ◽  
Filipe Portela ◽  
Manuel Filipe Santos
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Evaluation Model

scholarly journals Cloud Computing in Singapore: Key Drivers and Recommendations for a Smart Nation

2018 ◽  
Vol 6 (4) ◽  
pp. 39-47 ◽  
Author(s):  
Reuben Ng
Keyword(s):  
Public Policy ◽  
Cloud Computing ◽  
Big Data ◽  
Business Models ◽  
Fog Computing ◽  
Novel Technologies ◽  
Cloud Computing Adoption ◽  
Big Data Applications ◽  
Key Drivers ◽  
The Government

Cloud computing adoption enables big data applications in governance and policy. Singapore’s adoption of cloud computing is propelled by five key drivers: (1) public demand for and satisfaction with e-government services; (2) focus on whole-of-government policies and practices; (3) restructuring of technology agencies to integrate strategy and implementation; (4) building the Smart Nation Platform; (5) purpose-driven cloud applications especially in healthcare. This commentary also provides recommendations to propel big data applications in public policy and management: (a) technologically, embrace cloud analytics, and explore “fog computing”—an emerging technology that enables on-site data sense-making before transmission to the cloud; (b) promote regulatory sandboxes to experiment with policies that proactively manage novel technologies and business models that may radically change society; (c) on the collaboration front, establish unconventional partnerships to co-innovate on challenges like the skills-gap—an example is the unprecedented partnership led by the Lee Kuan Yew School of Public Policy with the government, private sector and unions.

Sign in / Sign up

Export Citation Format

Share Document