In recently years, data science technologies, such as big data, AI and machine learning, have attracted increasing attention in various networking domains, from network applications to network infrastructure, from telecom networks to enterprise networks, from service providers to equipment manufactures. This trend is enabled by advances and applications of big data technologies in communication networks, and increasing bodies of networked data have become available to be analyzed and mined. Therefore, there is an urgent demand of tools and products of exploiting this data to provide more intelligence in network operations and management.
Topics of interest include data science practices in areas of but not limited to:
Network monitoring, anomaly detection and localization, and root cause analysis
Anomaly detection and root cause analysis in time series of network data
Learning based network resource management and provisioning
IoT network design and optimization
SDN design and optimization
Cloud provisioning, management and troubling shooting
Customer profiling and behavior analysis, and retention
Deep learning applications in network operation and optimization
Big data system management in networks or networking applications
Graph computing for communication networks
Mobile application behaviors and recommendation
Business and operational transformation through big data and learning
Application of AI in network planning, optimization, and protocol design
AI-assisted power and energy management in network
Application of reinforcement learning in network management and control
Data-driven network architecture design
Network economics through machine learning
Cost and benefit tradeoff of running machine learning based networking applications
Security for machine learning applications in networking domain
Machine learning applications in edge computing
12月11日
2017
12月14日
2017
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