Big Data and Computational Intelligence in Networking-CRC(2018).pdf

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Recent years have witnessed a deluge of network data propelled by the emerging online social media, user-generated video contents, and global-scale communi- cations, bringing people into the era of big data. Such network big data holds much critical and valuable information including customer experiences, user behaviors, service levels, and other contents, which could significantly improve the efficiency, effectiveness, and intelligence on the optimization of the current Internet, facilitate the smart network operation and management, and help service providers and content providers reduce capital expenditure (CapEx) and opera- tional expenditure (OpEx) while maintaining a relatively high-level quality of service (QoS) and quality of experience (QoE). Typical examples of network intelligence received from network big data include rapid QoE impairment detection and mitigation, optimization of network asset utilization, proactive maintenance, rapid outage restoration, and graceful disaster recovery. These aims can be achieved from high-level computational intelligence based on emerging analytical techniques such as big data pro- cessing, Web analytics, and network analytics employing software tools from advanced analytics disciplines such as machine learning, data mining, and pre- dictive analytics. The computational intelligence for big data analysis is playing an ever-increasingly important role in supporting the evolution of the current Internet toward the next-generation intelligent Internet. However, the unstructured, heterogeneous, sheer volume and complex nature of network big data pose great challenges on the computational intelligence of these emerging analytical techniques due to high computational overhead and communication cost, non-real-time response, sparse matrix-vector multi- plications, and high convergence time. It is therefore of critical importance to understand network big data and design novel solutions of computational intelligence, scaling up f

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