What Should You Know About Big Data?

A large amount of big data is opening the era of data-driven solutions that will drive the development of communication networks. Current networks are usually designed based on static end-to-end design principles, and their complexity has increased dramatically in the past few decades, which hinders the effective and intelligent provision of big data. Big data networking and big data analysis in network applications both pose huge challenges to the industry and academic researchers.

Small devices continuously generate data, which is processed, cached, analyzed, and finally stored in network storage (such as routers), edge servers, or the cloud. Through them, users can effectively and safely discover and obtain big data for various purposes. Intelligent network technology should be designed to effectively support the distribution, processing, and sharing of such big data.

On the other hand, critical applications such as the Industrial Internet of Things, connected vehicles, network monitoring/security/management, etc. require fast mechanisms to analyze a large number of events in real-time, as well as offline analysis of a large amount of historical event data. These applications show strong demands to make network decisions (such as routing, caching, security, and slicing) intelligent and automated. In addition, the big data analysis techniques used to extract features and analyze large amounts of data places a heavy burden on the network, so smart and scalable methods must be conceived to make them practical.

Some issues related to big data intelligent networking and network big data analysis. Potential topics include but are not limited to:

1. Big data network architecture

2. Machine learning, data mining, and big data analysis in the network

3. Information-centric big data networking

4. Software-defined networking and network function virtualization for big data

5. The edge of big data, blur, and mobile edge computing

6. Security, trust, and privacy of big data networks

7. 5G and future mobile networks realize big data sharing

8. Blockchain for big data network

9. Data center network for big data processing

10. Data analysis of networked big data

11. Distributed monitoring architecture for networked big data

12. Machine learning can be used for network anomaly detection and security

13. In-network computing for intelligent networking

14. Big data analysis network management

15. Distributed Artificial Intelligence Network

16. Efficient networking of distributed artificial intelligence

17. Big data analysis and network traffic visualization

18. Big data analysis, intelligent routing, and caching

19. Big data networks in healthcare, smart cities, industries, and other applications

In the era of big data, Raysync provides ultra-fast, powerful, and high-speed large file transfer solutions to quickly respond to massive data transmission needs.