IEEE Workshop on Big Data and Machine Learning in Telecom (BMLIT)
Dec 5-8 (TBD), 2016, Washington DC, USA
@ http://icss2016.cqu.edu.cn/BMLIT/
In conjunction with the 2016 IEEE International Conference on Big Data
(Big Data 2016 @ http://cci.drexel.edu/bigdata/bigdata2016/)
Motivation
In recently years, big data technologies, aided with machine learning, has attracted increasing attention in telecom domain, both from the carrier side and the equipment manufacture side. As telecommunication networks develop and advance in a fast pace towards a more pervasive future, it has become obvious that operators are sitting on gold mines of networked data and there is an strong and urgent demand of tools and products of exploiting this data to provide more intelligence in telecom operations and customer management. In addition to operation logs of network elements, telecom data, especially data from cellular networks provide a wide variety of subscriber activity logs ranging from social activities such as calls and messaging, mobile payments, to multimedia streaming and gaming, with or without geographical information. The massive amount of telecom data offers network operators a unique opportunity to gain a more comprehensive picture of the network operation as well as their customers. Meanwhile, the advances in data processing and storage capabilities and machine learning techniques enable more applications as such. Towards this end, many efforts have been undertaken and therefore many questions arise such as:
• What data should be collected, for example, Netflows data, CDRs, DPI flow data, signaling data, etc.
• Where these data should be collected, at what network locations and at which network layers. For example, the same application data can be collected at base stations as well as distribution sites, with different level of information. As another example, the same data can be collected at layer 2 as well as layer 3, based on the OSI model.
• At what frequency these data should be collected and processed, for example, every 15 minute interval or every hour;
• How these data should be processed, in a central location or at where they are collected, or somewhere in the middle;
• What models to build and how often they should be updated;
• Whether the models are deployed online or offline, etc.
The workshop aims to bring together researchers, data scientists, computer scientists, and engineers in the area of telecom data analytics to share their ideas, technologies, and key results in all aspects of mining telecom data.
We intend to have a full-day workshop with one keynote talk, one or two invited talks, and seven to ten regular talks.
Topics
Topics of interest include but are not limited to:
• Performance monitoring in mobile wireless networks
• Telecom network log analysis and anomaly detection
• Root cause and causality analysis in time series of telecom data
• Telecom network monitoring
• IoT data for telecommunication
• Customer profiling and behavior analysis
• Churn analysis and customer retention
• Deep learning applications in network operation and optimization
• Big data system management in telecommunication
• Graph computing for telecommunication networks
• Mobile application behaviors and recommendation
• Big data and machine learning to assist business and operational transformation
• Data mining enabled communication network planning, optimization, and protocol design
Paper submission instructions
Important Dates
October 10, 2016: Due date for full workshop papers submission
November 1, 2016: Notification of paper acceptance to authors
November 15, 2016: Camera-ready of accepted papers
December 5-8 (TBD), 2016: Workshops
Review procedure

All submitted paper will be reviewed by 3 program committee members.
Workshop Organizers

Workshop Chairs
Jin Yang, Huawei Technologies, USA
Hui Zang, Huawei Technologies, USA
Li Liu, Chongqing University, China
Workshop Vice-Chair
Kai Yang, Huawei Technologies, USA

Technical Program Committee
Soshant Bali, AT&T Labs, USA
Li Chen, A*STAR, Singapore
Vijay Erramilli, Guavus, USA
Xin Liu, UC Davis, United States
Xiaoli Ma, Georgia Institute of Technology, USA
Sara Motahari, DoCoMo Labs, USA
Ye Ouyang, Verizon Wireless, USA
Dan Pei, Tsinghua University, China
Gyan Ranjan, Symantec, USA
Hai Shao, Verizon, USA
Ashwin Sridharan, AT&T Research, USA
Guoxin Su, National University of Singapore, Singapore
Tan Yan, NEC Labs America, USA
Kai Yang, Futurewei Technologies, USA
Hui Zhang, NEC Laboratories America, United States
Xiangliang Zhang, KAUST, Saudi Arabia