posted by organizer: yuzhiy || 7437 views || tracked by 19 users: [display]

BigMSN 2013 : 2013 IEEE International Workshop on Big Data Analytics in Mobile Social Networks

FacebookTwitterLinkedInGoogle

Link: http://asia2013.cloudcom.org/BigMSN2013
 
When Dec 16, 2013 - Dec 19, 2013
Where Fuzhou, China
Submission Deadline Sep 25, 2013
Notification Due Oct 11, 2013
Final Version Due Oct 25, 2013
Categories    social computing   data mining   social networks   ubiquitous computing
 

Call For Papers

As smart phones, global positioning systems, and Web 2.0 technologies become prevalent, mobile social networks (MSN), including location based social networks, are getting popular, e.g., Foursquare, BrightKite, Jiepang, Facebook, Google+, Flickr, Wechat all offer location sharing services. MSN bridge the physical and virtual worlds by enabling anytime anywhere social interaction and geotagging any user generated content. It is believed that various application domains will be benefited from analyzing the data from MSN, such as business services, health care, disaster management, smart cities, etc.. However, since they reflect the daily social lives of billions of people, MSN data are not only of vast amount, but also with characteristics distinguished from general big data: 1) complex network structures from the social dimension; 2) sparse or coarse trajectories from the spatial dimension; 3) subjective bias from human factors. Therefore it is imperative to develop sophisticated and scalable analyzing techniques for extracting meaningful information from MSN Data.

To address these concerns, the workshop BigMSN 2013 is intended to provide a forum for researchers to present their researches related to big data analytics in MSN. We solicit technical papers of topics including but not limited to:
* Location estimation / prediction from MSN data
* User / group mobility modeling
* Activity / emotion / opinion recognition from MSN data
* Data mining / machine learning / pattern recognition in MSN
* Algorithms for mobile / crowd / participatory sensing
* Social structure and community detection in MSN
* Dynamics and evolution of MSN
* Storing and querying of large scale graph / spatial data
* Location based personalized search / recommendation
* Energy / bandwidth / time efficient strategy for MSN
* Security, privacy, trust and reputation in MSN
* Software architecture for MSN data analytics lifecycle
* Visualization of MSN data
* Innovative location-based services and applications
* User studies and experiences


Interested authors can submit full papers of 6 pages in IEEE Computer Society Proceedings format to https://www.easychair.org/account/signin.cgi?conf=bigmsn2013. Each paper will receive at least 3 peer reviews. Accepted papers will be included in the CloudCom-Asia workshop proceedings (EI-Indexed), published by IEEE Computer Society Press. At least one author of each accepted paper should register as a participant and present the paper on the workshop. Extended versions of distinguished papers will be invited for publication in special issues of reputable international journals:
* International Journal of Communication Systems (Wiley) (SCI, EI)
* Journal of Systems Architecture (Elsevier) (SCI, EI)
* Automated Software Engineering (Springer) (SCI, EI)
* Journal of Internet Technology (SCIE, EI)


Workshop Chairs:
* Zhiyong Yu, Fuzhou University, China
* Sarah Gallacher, University College London, UK
* Jianliang Xu, Hong Kong Baptist University, Hong Kong

Workshop Vice-chair:
* Daqiang Zhang, Tongji University, China

Technical Program Committee:
* Bin Guo, Northwestern Polytechnical University, China
* Chris Smith-Clarke, University College London, UK
* Damian Philipp, University of Stuttgart, Germany
* Daqiang Zhang, Tongji University, China
* De-Nian Yang, Academia Sinica, Taiwan
* Dingqi Yang, Institut Mines-Telecom, France
* Feng Xia, Dalian University of Technology, China
* Haoyi Xiong, Institut Mines-Telecom, France
* Jianliang Xu, Hong Kong Baptist University, Hong Kong
* Kazutoshi Sumiya, University of Hyogo, Japan
* Martin Atzmueller, University of Kassel, Germany
* Pravin Shankar, Green Dot Corporation, USA
* Santi Phithakkitnukoon, The Open University, UK
* Sarah Gallacher, University College London, UK
* Shijian Li, Zhejiang University, China
* Xiangwen Liao, Fuzhou University, China
* Xiaodi Huang, Charles Sturt University, Australia
* Xiaofei Liao, Huazhong University of Science and Technology, China
* Xufei Wang, LinkedIn Corporation, USA
* Zhiyong Yu, Fuzhou University, China

Related Resources

IEEE ICCCBDA 2023   IEEE--2023 the 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA 2023)
MLDM 2023   18th International Conference on Machine Learning and Data Mining
ICCCBDA 2023   IEEE--2023 the 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA 2023)
SDM 2023   SDM 2023 : SIAM International Conference on Data Mining
IEEE SSCI 2023   2023 IEEE Symposium Series on Computational Intelligence
CoMSE 2023   2023 International Conference on Materials Science and Engineering (CoMSE 2023)
ICCSN 2023   IEEE--2023 15th International Conference on Communication Software and Networks (ICCSN 2023)
IEEE-ADMIT 2022   2022 International Conference on Algorithms, Data Mining, and Information Technology (ADMIT 2022)
IEEE ICAIBD 2023   2023 IEEE The 6th International Conference on Artificial Intelligence and Big Data (ICAIBD 2023)
CSML 2023   International Conference on Computer Science and Machine Learning