scholarly journals The Business Process Reconstruction of Railway-River Combined Transportation Cloud Platform Taking China Container Export as an Example

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Furong Jia ◽  
Lin Sun ◽  
Jiaxin Yuan ◽  
Yongping Li ◽  
Qiang Huang
Keyword(s):  
Big Data ◽  
Information Sharing ◽  
Water Transport ◽  
Intermodal Transportation ◽  
Cloud Platform ◽  
Information Process ◽  
Practical Support ◽  
Low Degree ◽  
Technical Issues ◽  
Process Reconstruction

In recent years, major large ports in China have realized the business informatization of rail-water intermodal transportation. However, the overall development level of intermodal transportation informatization has been restricted to a great extent due to the unbalanced development of intermodal transportation informatization in different regions, the rigid system architecture, the low degree of information sharing, and the lack of data management and analysis methods. Combined with the structure and business characteristics of intermodal transportation information systems, adopting cloud computing and Big Data technology, we propose an intermodal transportation information process with waybill as the information carrier and FPMS as the service fulcrum in this paper. Illustrated by the example of China’s container export process, this paper explores a series of key technical issues in the cloud environment, such as application management, business information sharing, and Big Data processing, at different levels of the construction of the rail water transport cloud platform, combined with its business characteristics, and makes experimental analysis on the relevant models to verify the feasibility of the reconstruction of the rail water transport cloud platform. It can provide theoretical and practical support for the development of rail water intermodal informatization in China.

Low-cost IoT, Big Data, and Cloud Platform for Developing Countries

2017 ◽  
pp. 285-299 ◽  
Author(s):  
Corentin Dupont ◽  
Tomas Bures ◽  
Mehdi Sheikhalishahi ◽  
Congduc Pham ◽  
Abdur Rahim
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Low Cost ◽  
Cloud Platform

Information Sharing in VANETs

2010 ◽  
pp. 75-96
Author(s):  
Marco Fiore ◽  
Claudio Casetti ◽  
Carla-Fabiana Chiasserini
Keyword(s):  
Information Sharing ◽  
Data Exchange ◽  
Ad Hoc ◽  
Network Simulator ◽  
Content Sharing ◽  
Content Caching ◽  
Peer Network ◽  
Future Challenges ◽  
Technical Issues ◽  
Peer To Peer Network

This chapter looks at a vehicular ad hoc network (VANET) as a peer-to-peer network, where mobile users may request information contents as well as provide them to other nodes, and it addresses the major technical issues that emerge when dealing with information sharing in VANETs. After briefly reviewing some proposals appeared in the literature on application and network protocols for data exchange in VANETs, the chapter focuses on a possible application for data sharing between vehicular users, which exploits the pull-based approach. It then highlights the main challenges in such a scenario and introduces some mechanisms that can be applied to solve two major issues in content sharing: content query propagation and content caching. A comparison among the schemes presented for query propagation, as well as between the mechanisms introduced for data caching, is shown through simulation results derived using the network simulator ns2. Finally, future challenges and emerging research topics for content sharing and dissemination in VANETs are outlined.

scholarly journals Wrinkle-Enabled Highly Stretchable Strain Sensors for Wide-Range Health Monitoring with a Big Data Cloud Platform

2020 ◽  
Vol 12 (38) ◽  
pp. 43009-43017 ◽  
Author(s):  
Jian Huang ◽  
Jian Zhou ◽  
Yangmei Luo ◽  
Gan Yan ◽  
Yi Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Health Monitoring ◽  
Strain Sensors ◽  
Cloud Platform ◽  
Wide Range ◽  
Highly Stretchable

scholarly journals Big Data Management for Cloud-Enabled Geological Information Services

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yueqin Zhu ◽  
Yongjie Tan ◽  
Xiong Luo ◽  
Zhijie He
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Information Services ◽  
Cloud Platform ◽  
Application Development ◽  
Geological Information ◽  
Technology Applications ◽  
Massive Scale ◽  
Application Requirements

Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.

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