IEEE Workshop on Big Data and Machine Learning in Telecom (BMLIT)

Dec 5, 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/)

 

Workshop Program

Workshop on Big Data and Machine Learning in Telecom

Time

Title

Presenter/Author

8:00am – 8.05am

Introduction

8:05am - 8:50am

Keynote Speech: Big Data Analytics in Mobile Environments

Hui Xiong

8:50am - 9:35am

Empower Analytics in Operators Data Ecosystem

Ye Ouyang

9:35am - 10:00am

Preliminary Big Data in a 5G Test Network

Teemu Kanstrn, Jussi Liikka, Jukka Mäkelä, Markus Luoto, and Jarmo Prokkola

10:00am - 10:20am

Coffee Break

10:20am - 10:45am

Quick Model Fitting Using a Classifying Engine

Yiming Kong, Hui Zang, and Xiaoli Ma

10:45am - 11:10am

WHAT: A Big Data Approach for Accounting of Modern Web Services

Martino Trevisan, Idilio Drago, Marco Mellia, Han Hee Song, and Mario Baldi

11:10am - 11:35am

Spark-based Rare Association Rule Mining for Big Datasets

Ruilin Liu, Kai Yang, Yanjia Sun, Tao Quan, and Jin Yang

11:35am – 12.00pm

Evaluating Machine Learning Algorithms for Anomaly Detection in Clouds

Anton Gulenko, Marcel Wallschläger, Florian Schmidt, Odej Kao, and Feng Liu

 

 

Keynote Speakers

Professor Hui Xiong

Email:
hxiong@rutgers.edu
WEB:
http://datamining.rutgers.edu
Management Science and Information Systems Department
Rutgers Business School
Rutgers, the State University of New Jersey

˵: Macintosh HD:Users:hxiong:Documents:Hui:PersonalRecord:Hui2016Photo:Hui_Xiong_Hi.JPG

 

Dr. Hui Xiong is currently a Full Professor and the Vice Chair in the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance, at the Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), the ICDM-2011 Best Research Paper Award (2011), a RBS Deans Research Professor Award (2016), and an IBM ESA Innovation Award (2008). Dr. Xiong is also a Distinguished Visiting Professor (Grand Master Chair Professor) at the University of Science and Technology of China (USTC). For his outstanding contributions to data mining and mobile computing, he was elected an ACM Distinguished Scientist in 2014.

Dr. Xiong received his Ph.D. in Computer Science from the University of Minnesota (UMN), USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), Hefei, China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has published prolifically in refereed journals and conference proceedings (4 books, 70+ journal papers, and 130+ conference papers. He is an Associate Editor of IEEE Transactions on Data and Knowledge Engineering (TKDE), IEEE Transaction on Big Data (TBD), ACM Transactions on Knowledge Discovery from Data (TKDD), and ACM Transactions on Management Information Systems (TMIS). He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the 2013 IEEE International Conference on Data Mining (ICDM-2013), and a General Co-Chair for the 2015 IEEE International Conference on Data Mining (ICDM-2015).

 

Dr. Ye Ouyang

Dr. Ye Ouyang is the youngest Fellow in Verizon history, leading a research and development team in Verizon Headquarters working on the forefront of cutting edge wireless networks, devices, and big data analytics field. 
Dr. Ouyang has over 15 years experience in telecom industry. His research lies in big data analytics for wireless networks and devices, with a focus on 4G LTE network and device perf., capacity, traffic patterns, user behaviors, and network & device service quality.
Dr. Ouyang serves as corporate representative, industry Chair, and standing committee member in different organizations including 3GPP, GSMA, TMF (Telecom Mgmt Forum), and IEEE.

In 2016, Dr. Ouyang as PI and his team was awarded 2016 US Telecom Top10 Innovation Awards by Fierce due to his significant contribution in inventing Verizon VoLTE Quality (VVQ) standard and system.
In 2012-13, he as Co-PI was awarded telecom big data research funding by White House, the office of Science and Technology.

He authored 20+ academic papers, 3 books, and 17 US Patents.  Dr. Ouyang holds a Master of Science from Tufts University, a Master of Science from Columbia University, and a Doctor of Philosophy from Stevens Institute of Technology.

 

 

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

October 17, 2016: Due date extended

November 1, 2016: Notification of paper acceptance to authors

November 15, 2016: Camera-ready of accepted papers

December 5, 2016: Workshops

Review procedure

 

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

 

Paper Submission

Please submit a full-length paper up to 6 page IEEE 2-column format.
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below).
Formatting Instructions 8.5" x 11"
(DOC, PDF)
LaTex Formatting Macros

Please submit your paper via the following link:

https://wi-lab.com/cyberchair/2016/bigdata16/scripts/submit.php?subarea=S10&undisplay_detail=1&wh=/cyberchair/2016/bigdata16/scripts/ws_submit.php

 

Invited talks

To be announced

Accepted Papers

Time of workshop: 8AM C 12PM, December 5, 2016.

Accepted papers:

S10202

Teemu Kanstrn, Jussi Liikka, Jukka Mäkelä, Markus Luoto, and Jarmo Prokkola, Preliminary Big Data in a 5G Test Network

Teemu Kanstrn (VTT, Finland), Jussi Liikka (VTT, Finland), Jukka Mäkelä (VTT, Finland), Markus Luoto (VTT, Finland), and Jarmo Prokkola (VTT, Finland)

S10203

Martino Trevisan, Idilio Drago, Marco Mellia, Han Hee Song, and Mario Baldi, WHAT: A Big Data Approach for Accounting of Modern Web Services

Martino Trevisan (Politecnico di Torino, Italy), Idilio Drago (Politecnico di Torino, Italy), Marco Mellia (Politecnico di Torino, Italy), Han Hee Song (Cisco, Inc., USA), and Mario Baldi (Politecnico di Torino, Italy)

S10205

Yiming Kong, Hui Zang, and Xiaoli Ma, Quick Model Fitting Using a Classifying Engine

Yiming Kong (Georgia Institute of Technology, USA), Hui Zang (Futurewei Technologies, Inc, USA), and Xiaoli Ma (Georgia Institute of Technology, USA)

S10206

Terrence Fries, Telecom Network Anomaly Detection using Fuzzy Clustering

Terrence Fries (Indiana University of Pennsylvania, USA)

S10207

Ruilin Liu, Kai Yang, Yanjia Sun, Tao Quan, and Jin Yang, Spark-based Rare Association Rule Mining for Big Datasets

Ruilin Liu (Huawei Technologies Inc., USA), Kai Yang (Tong Ji University, China), Yanjia Sun (Huawei Technologies Inc., USA), Tao Quan (Huawei Technologies Inc., China), and Jin Yang (Huawei Technologies Inc., USA)

S10209

Anton Gulenko, Marcel Wallschläger, Florian Schmidt, Odej Kao, and Feng Liu, Evaluating Machine Learning Algorithms for Anomaly Detection in Clouds

Anton Gulenko (TU Berlin, Germany), Marcel Wallschläger (TU Berlin, Germany), Florian Schmidt (TU Berlin, Germany), Odej Kao (TU Berlin, Germany), and Feng Liu (European Research Center, Huawei, Germany)

 

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