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Welcome to IEEE Technical Committee on Big Data
IEEE SIG on Big Data Intelligent Networking
Chair: Ruidong Li, National Institute of Information and Communications Technology (NICT), Japan, Email: [email protected]
Vice-Chair: Houbing Song, Embry-Riddle Aeronautical University, USA, Email: [email protected]
Vice-Chair: Xiaoming Fu, Gottingen University, Germany, Email: [email protected]
Vice-Chair: Kui Wu, University of Victoria, Canada, Email: [email protected]
Vice-Chair: Payam Barnaghi, University of Surrey, UK, Email: [email protected]
Vice-Chair: Zhou Su, Shanghai University, China, Email: [email protected]
Vice-Chair: Constandinos Mavromoustakis, University of Nicosia, Cyprus, Email: [email protected]
Advisor: Jiannong Cao, Hong Kong Polytechnic University, Hongkong, Email: [email protected]
Advisor: Hsiao-Hwa Chen, National Cheng Kung University, Taiwan, Email: [email protected]
Advisor: Wenjing Lou, Virginia Tech, USA, Email: [email protected]
Advisor: Chonggang Wang, Interdigital, USA, Email: [email protected]
Advisor: Jie Wu, Temple University, USA, Email: [email protected]
Advisor: Hitoshi Asaeda, National Institute of Information and Communications Technology (NICT), Japan, Email: [email protected]
Scope and Objectives
Big data are transforming the world and open the era of the new paradigm for science discovery through data-driven approach. This paradigm also brings out great influences on networking research area. The current networks are designed based on the static end-to-end design principle, and their complexity has dramatically increased in the past several decades, which hinders the efficient and intelligent provision of big data services and makes it important and challenging to design network applications based on big data and in-network computation. That is, both networking for big data (e.g., collection, computation, analysis, and visualization) and big data analytics and in-network computation for networking applications (e.g. monitoring, routing, caching, and security) show great challenges for industries and academia.
Regarding networking for big data, the big data mining and learning applications depend on the efficient and effective support from the underneath networking protocols. Big data are collected from small devices, processed/cached/analyzed in the network, and finally stored at the servers or clouds. The big data applications involve the data sources from different geographically distributed data centers or in-network storages. Huge amount of users efficiently and securely search, discover and fetch the big data from the data centers or in-network storages. Regarding big data analytics for networking, critical applications such as network monitoring, network security or dynamic network management require fast mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. The applications making networking decisions (e.g. routing, caching, and security) from the ever-growing amount of measurement data is becoming a big challenge, which remains poorly understood and investigated. Furthermore, big data analytic techniques to characterize, detect, locate and analyze complex behaviors bring out much burden for networking, and thus the smart and scalable approaches must be conceived to enable them to be practical. The analysis on the network status data shows the great potential to improve the performance of networking and applications.
In summary, this SIG will focus on the technical challenges and applications of intelligent networking for and by big data and in-network computation. We envision that the combination of big data with networking will provide more efficient support for big data applications and enable more intelligent networking applications. The areas of interests include, but are not limited to, the following:
• Networking architecture for big data
• Big data with in-network computation
• Networking big data analysis
• Machine learning, data mining and big data analytics in networking
• Deep learning for networking
• Information-centric networking for big data
• Software-defined network for big data
• Networking for distributed machine learning
• Queueing theory analysis for big data applications
• Edge, fog, and mobile edge computing for big data
• Privacy and trust management for big data networking
• Authentication, authorization, accountability for big data networking
• Sensor, drone, ad-hoc networks for big data collection and distribution
• 5G and future mobile networks for big data sharing
• Performance modeling in networking for big data
• Mobility and big data
• Network virtualization for big data
• Blockchain with big data networking
• Big data analytics for blockchain
• Big data for disaster-resilient networking
• Data-center network for big data processing
• Application of reinforced-learning for networking
• Data analytics for network measurement data mining
• Big data analysis frameworks for network monitoring data
• Distributed monitoring architectures for networking big data
• Networking-based benchmarks for big data analysis
• Machine learning for network anomaly detection and security
• Network anomaly diagnosis through networking big data
• In-network computation for intelligent networking
• Big data analytics for network management
• Distributed artificial intelligence for networking
• Efficient networking for distributed artificial intelligence
• Big data analytics and visualization for network traffic
• Research challenges on big data analytics for networking
• Big data analytics for intelligent routing and caching
Founding Members
- Ruidong Li, National Institute of Information and Communications Technology (NICT), Japan
- Houbing Song, Embry-Riddle Aeronautical University, USA,
- Constandinos Mavromoustakis, University of Nicosia, Cyprus
- Zhou Su, Shanghai University, China
- Kui Wu, University of Victoria, Canada
- Burak Kantarci, University of Ottawa, Canada
- Jie Wu, Temple University, USA
- Hsiao-Hwa Chen, National Cheng Kung University, Taiwan
- Xiaoming Fu, Gottingen University, Germany
- Rongxing Lu, University of New Brunswick, Canada
- Guido Dartmann, University of Applied Sciences Trier, Germany
- Jie Li, Shanghai Jiao Tong University, China
- Anke Schmeink, RWTH Aachen University, Germany
- Mohammad Shojafar, University of Padua, Italy
- Jinsong Wu, University of Chile, Chile
- Hai Jin, Huazhong University of Science and Technology, China
- Payam Barnaghi, University of Surrey, UK,
- Jiannong Cao, Hong Kong Polytechnic University, Hongkong
- Wei Bao, University of Sydney, Australia
- Xiaojiang Du, Temple University, USA
- Panlong Yang, University of Science and Technology of China, China
- Lin Cai, University of Victoria, Canada
- Guang Cheng, Southeast University, China
- Chen Qian, University of California Santa Cruz, USA
- Jun Bi, Tsinghua University, China
- Chonggang Wang, Interdigital, USA
- Liming Sun, Chinese Academy of Sciences, China
- Alex Liu, Michigan State University, USA
- Hitoshi Asaeda, National Institute of Information and Communications Technology (NICT), Japan
- Qitao Gan, Telenor, Norway
- Kai Lei, Peking University, China
- Wenjing Lou, Virginia Tech, USA
- Bin Xiao, Hong Kong Polytechnic University, Hongkong
- Dongming Peng, University of Nebraska-Lincoln, USA
- Kristian Skracic, Ericsson Nikola Tesla d.d., Croatia
- Yingfei Dong, University of Hawaii, USA
- Hongjian Sun, Durham University, U.K.
- Yu Jiang, Tsinghua University, China
- Jian Tang, Syracuse University, USA
- Dan Wang, Hong Kong Polytechnic University, Hongkong
- Minho Jo, Korea University, Korea
- Yong Cui, Tsinghua University, China
- Takuji Tachibana, University of Fukui, Japan
- Mo Sha, State University of New York at Binghamton, USA
- Hui Zang, Huawei Research, USA
- Feng Ye, University of Dayton, USA
- Elias Bou-Harb, Florida Atlantic University, USA
- Jongwon Kim, Gwangju Institute of Science and Technology, Korea
- Humphrey Rutagemwa, Communications Research Centre, Canada
- Yuki Koizumi, Osaka University, Japan
- Dingde Jiang, University of Electronic Science and Technology of China, China
- Miao Pan, University of Houston, USA
- Ali M. Al-Salim, University of Leeds, U. K.
- Yunfei Ma, Massachusetts Institute of Technology, USA
Linkedin HP:
https://www.linkedin.com/groups/8673834