BigMUD 2013 - The First International Workshop on Mining and Understanding from Big Data

The First International Workshop on Mining and Understanding from Big Data (BigMUD 2013)
(http://kdd.csd.uwo.ca/BigMUD.html)
In conjunction with
IEEE International Conference on Data Mining (ICDM 2013)
Dallas, Texas?December 8-11, 2013
Introduction
Big data refers to datasets that exceed the competence of commonly used IT systems in terms of processing space and/or time. Traditionally, massive data are mostly produced in scientific fields such as astronomy, meteorology, genomics physics, biology, and environmental research. Due to the rapid development of IT technology and the consequent decrease of cost on collecting and storing data, big data has been generated from almost every industry and sector as well as governmental department, including retail, finance, banking, security, audit, electric power, healthcare, to name a few.
Currently, big data is often on the order of petabytes and even exabytes. However, big data has become bigger and bigger not only in its size, but also in its growth rate and variety. The volume of big data often grows exponentially or even in rates that overwhelm the well-known Moore’s Law. Meanwhile, big data has been extended from traditional structured data into semi-structured and completely unstructured data of different types, such as text, image, audio, video, click streams, log files, etc. Moreover, big data is often internally interconnected and thus form complex data/information networks.
Although big data can offer us unprecedented opportunities, they also pose many grand challenges. Due to the massive volume and inherent complexity, it is extremely difficult to store, aggregate, manage, and analyze big data and finally mine valuable information/knowledge from the complex data/information networks. Therefore, in the presence of big data, the models, algorithms and methods for traditional data mining become no longer effective and efficient. For instance, similarity learning, upon which various similarity-based tasks (e.g., ranking and clustering) can be launched, is extremely challenging for real applications with big data due to their typical features such as the data being heterogeneous, time-evolving, sparse and noisy. On the other hand, some data is generated exponentially or super-exponentially in a streaming manner. Therefore, how to carry out real-time analysis on, and deep mining and understanding from big data so as to obtain dynamical and incremental information/knowledge, is another grand challenge. In general, at the era of big data, it is expected to develop new models, algorithms, methods, and even paradigms for mining, analyzing, and understanding big data.
This workshop aims to provide a networking venue that will bring together scientists, researchers, professionals, and practitioners from both industry and academia and from different disciplines (including computer science, social science, network science, etc.) to exchange ideas, discuss solutions, share experiences, promote collaborations, and report state-of-the-art research results and technological innovations on various aspects of mining and understanding from big data.
Scope and Topics
The topics of interest include, but are not limited to:
? Acquisition, representation, indexing, storage, and management of big data
? Processing, pre-processing, and post-processing of big data
? Models, algorithms, and methods for big data mining and understanding
? Knowledge discovery and semantic-based mining from big data
? Metric/similarity learning for big data
? Visualizing analytics and organization for big data
? Context data mining from big Web data
? Social computing over big Web data (e.g., network analysis, community detection)
? Industrial and scientific applications of big data mining such as search and recommendations
Important Dates
Submission Deadline: August 3, 2013
Authors Notification: September 24, 2013
Workshop Date: December 8, 2013
Paper Submission Guideline
All papers need to be submitted electronically through the conference website (https://wi-lab.com/cyberchair/2013/icdm13/scripts/...) with PDF format. The materials presented in the papers should not be published or be under submission elsewhere. Each paper is limited to 8 pages including figures and references and follows the IEEE ICDM format requirements (http://icdm2013.rutgers.edu/author-instructions).
Once accepted, the paper will be included into the conference proceedings published by IEEE Computer Society Press (indexed by EI). At least one of the authors of any accepted paper is requested to register the paper at the workshop.
Workshop Co-Chairs (Ordered with the last name alphabetized)
? Xueqi Cheng, Institute of Computing Technology, CAS, China, cxq-AT-ict.ac.cn
? Alvin Chin, Nokia, China, alvin.chin-AT-nokia.com
? Charles X. Ling, Western University, Canada, cling-AT- csd.uwo.ca
? Fei Wang, IBM T. J. Watson Research Center, USA, fwang-AT-us.ibm.com
Organizing committee
Jilei Tian Nokia, China
Guanling Chen University of Massachusetts Lowell, USA,
Enhong Chen University of Science and Technology of China
Jun Wang IBM T.J. Watson Research Center
Peng Cui Tsinghua University, China
Irwin King Chinese University of Hong Kong, China
Program Committee
Lada Adamic University of Michigan, USA
Edward H. Chi Google, USA
Nick Craswell Microsoft, USA
Jiafeng Guo Chinese Academy of Sciences, China
Jake Hofman Yahoo! Research, USA
Xiaolong Jin Chinese Academy of Sciences, China
Ravi Kumar Yahoo! Research, USA
Ronny Lempel Yahoo!, Israel
Jure Lescovec Stanford University, USA
Jianzhong Li Harbin Institute of Technology, China
Tie-Yan Liu Microsoft Research Asia, China
Qiaozhu Mei University of Michigan, USA
Donald Metzler University of Southern California, USA
Huawei Shen Chinese Academy of Sciences, China
Luo Si Purdue University, USA
Jie Tang Tsinghua University, China
Dacheng Tao University of Technology Sydney, Australia
Jun Wang University College London, UK
Rong Yan Facebook, USA
Jinbo Bi, University of Connecticut
Heng Huang, University of Texas at Arlington
Jingu Kim, Nokia, USA
Brian Kulis, Ohio State University
Ee-Peng Lim, Singapore Management University
Zoran Obradovic, Temple University
Haesun Park, Georgia Institute of Technology
Vikas Sindhwani, IBM T. J. Watson Research Center
David Sontag, New York University
Kush R. Varshney, IBM T.J. Watson Research Center
Jieping Ye, Arizona State University
Shipeng Yu, Siemens Solutions
Dan Zhang, Facebook
Yu Zheng, Microsoft Research Asia, Beijing, China
Zhiwen Yu, Northwestern Polytechnic University, Xian, China
Zheng Yan, Xidian University China and Aalto University, Finland
Antti Eronen, Nokia Research Center, Tampere, Finland
Yiqiang Chen, Chinese Academy of Science, Beijing, China
Li Chen, Hong Kong Baptist University, Hong Kong, China
Vincent Zheng, Hong Kong University of Science and Technology, Hong Kong, China
Linli Xu, University of Science and Technology of China, China
Daeyoung Kim, KAIST, Korea
Hongbo Deng, Yahoo! Labs, USA
Yoshiharu Ishikawa, Nagoya University, Japan