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CFP -- Mining and Understanding from Big Data (BigMUD 2013) ===========================================================================       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)                     (http://icdm2013.rutgers.edu/)                   Dallas, TexasĄ¡èDecember 8-11, 2013 (Distinguished papers presented at the workshop, after further extension and revision, will appear in a Special Issue of the SCI-indexed Journal    - Journal of Computer Science and Technology (JCST), Springer-        http://jcst.ict.ac.cn:8080/jcst/EN/volumn/home.shtml) ============================================================================ 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. Recently, big data over the Web (big Web data for short), which includes all the context data, such as, user generated contents, browser/search log data, deep web data, etc., have attracted extensive interests, as these context data and their analyses help us to understand what is happening in real life. This can help to enable new ways for improving user experience by providing more accurate predictions and recommendations thus creating a personalized smarter internet. 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 MooreĄŻs 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/submit.php?subarea=DM) 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. Distinguished papers presented at the workshop, after further extension and revision, will appear in a Special Issue of the SCI-indexed Journal, namely, Journal of Computer Science and Technology (JCST), Springer (http://jcst.ict.ac.cn:8080/jcst/EN/volumn/home.shtml). Workshop Co-Chairs: -------------------- - Xueqi Cheng, Institute of Computing Technology, CAS, China, cxq@ict.ac.cn - Alvin Chin, Nokia, China, alvin.chin@nokia.com - Charles X. Ling, Western University, Canada, cling@ csd.uwo.ca - Fei Wang, IBM T. J. Watson Research Center, USA, fwang@us.ibm.com Organizing committee: --------------------- Jilei Tian Nokia Research Center, 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: ------------------ (To be added)    Êղؠ  ·ÖÏí   ¶¥(0)