Download World Wide Web journal

World Wide Web journal Internet and Web Information Systems ~Special Issue Call for Papers~
Title： Deep Mining Big Social Data
GUEST EDITORS: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell
The internet revolution has made information acquisition easy and cheap so that it has been producing massive web/social data in our real life. The emergence of big social media has lead researchers to study the possibility of their exploitation in order to identify hidden knowledge. However, a huge number of issues appear in obtained big social data. First, there are incomplete social data due to all kinds of reasons, such as security and private information. Second, the structure of social data is different, including structured data (e.g., social web data), semi‐structured data (e.g., XML data) and unstructured data (e.g., social networks). Third, the web data are often high‐dimensional. However, current computer techniques can only deal with structured, complete and moderate‐sized‐dimensional data. Moreover, current computer technologies can only mine the basic structure and are not capable of mining their natural complex structure (or deep structure). Hence, there is a huge gap between existing technologies and the real requirements of actual big social data. In this case, deep mining of big social data (such as data preprocessing, deep pattern discovery, pattern fusion, and outlier/noise detection) stands as an interesting promise to relief such a gap. The World Wide Web journal invites papers for a special issue on “Deep Mining Big Social Data” to attract articles that cover existing approaches to mining big social data. Below is an incomplete list of potential topics to be covered in the special issue:  Deep mining techniques for big social web data  Retrieval methods for social/multimedia web information retrieval  Kernel‐based learning for multi‐modality web health data  Incremental learning or online learning for web data.  Data fusion for multi‐source web data  Cloud data mining  Data management and mining in social Web  Deep mining web advertising and community analysis  Multimedia web search and meta‐search  Pattern discovery from blogs, micro‐blogs, and twitter mining  Dynamic graph mining for big social data  Visualization and search user interfaces and interaction for social data IMPORTANT DATES:
Paper submission deadline: June 30, 2017 First notification: August 15, 2017 Revision: September 15, 2017 Final decision: December 1, 2017 GUEST EDITOR BIOS:
Xiaofeng Zhu is Postdoctoral Research Associate at the University of North Carolina at Chapel Hill. His research interests include machine learning and pattern recognition. He has published over 70 papers including journals and conferences. (continued on page 2) PAPER SUBMISSION:
 Authors are encouraged to submit high‐quality, original work that has neither appeared in, nor is under consideration by, other journals.  All papers will be reviewed following standard reviewing procedures for the Journal.  Papers must be prepared in accordance with the Journal guidelines: http://www.springer.com/11280 .  Submit manuscripts to: http://WWWJ.edmgr.com. World Wide Web Journal www.Springer.com/11280
Editors‐in‐Chief: M. Rusinkiewicz; Y. Zhang
Published by Springer. 


Gerard Sanroma is a (Marie Curie) research associate at SIMBIOsys group (UPF). His research interests are machine learning, medical imaging, computer vision and structural pattern recognition. He has published over 30 papers including book chapters, journals and conferences. Jilian Zhang is a special‐term professor at Guangxi University of Finance and Economics, China. His research interests include data mining, data management, and query processing. His research work has been published in IEEE Transactions on Knowledge and Data Engineering, Information Systems, ACM SIGMOD, VLDB, and IJCAI. Brent C. Munsell is an assistant professor in the Department of Computer Science at the College of Charleston. His research interests are in machine learning, medical image analysis, and computer vision. He has published papers in Nature, IEEE Transaction on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, NeuroImage, and IEEE Transactions on Medical Imaging.