Introduction
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Due to the rapid development of IT technology including Internet, Cloud Computing, Mobile Computing, and Internet of Things, as well as 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. 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 various types, such as text, image, audio, video, click streams, log files, etc.

It is no doubt that big data can offer us unprecedented opportunities. However, it also poses 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 theories, models, algorithms and methods of traditional data related fields, such as, data mining, data engineering, machine learning, statistical learning, computer programming, pattern recognition and learning, visualization, uncertainty modeling, and high performance computing etc., become no longer effective and efficient. On the other hand, some data is generated exponentially or super-exponentially in a streaming manner. Therefore, how to delicately analyze and deeply understand big data so as to obtain dynamical and incremental information / knowledge, is a grand challenge. In general, at the era of big data, it is expected to develop new theories, models, algorithms, methods, and paradigms for mining, analyzing, and understanding big data, and even a new inter-discipline, Data Science, for studying the perception, acquisition, transportation, storage, management, analysis, visualization, and applications of big data, and finally implement the transformation from data to knowledge.

DSBDA 2016 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 work on various aspects of data science and big data analytics.

Topics
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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
* Visualizing analytics and organization for big data
* Context data mining from big Web data
* Social computing over big Web data
* Industrial and scientific applications of big data
* Tools for big data analytics


Submissions
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The page limit of workshop papers is 8 pages in the standard IEEE 2-column format (http://www.ieee.org/conferences_events/conferences/publishing/templates.html), including the bibliography and any possible appendices. All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2016 submission guidelines available at http://icdm2016.eurecat.org/. Papers should be submitted in PDF format, electronically, using the CyberChair submission system: https://wi-lab.com/cyberchair/2016/icdm16/scripts/ws_submit.php?subarea=S.

Note that all accepted papers will be included in the IEEE ICDM 2016 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS). Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

Important Dates
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Submissions Due Date: August 12, 2016
Notifications of Acceptance: September 13, 2016
Camera-Ready Deadline: September 20, 2016
Workshop Date: December 12, 2016

Steering Committee
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Prof. Benjamin W. Wah, Chinese University of Hong Kong, China
Prof. Jinpeng Huai, Beihang University, China
Prof. Xueqi Cheng, Institute of Computing Technology, Chinese Academy of Sciences, China

Workshop Chairs
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Dr. Xiaolong Jin
CAS Key Lab of Network Data Science and Technology,
Institute of Computing Technology, Chinese Academy of Sciences (CAS), China
Email: jinxiaolong@ict.ac.cn

Dr. Jiafeng Guo
CAS Key Lab of Network Data Science and Technology,
Institute of Computing Technology, Chinese Academy of Sciences (CAS), China
Email: guojiafeng@ict.ac.cn

Dr. Huewei Shen
CAS Key Lab of Network Data Science and Technology,
Institute of Computing Technology, Chinese Academy of Sciences (CAS), China
Email: shenhuawei@ict.ac.cn

Program Committee
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* Nan Cao, New York University, China
* Zhicheng Dou, Renmin University of China, China
* Chong Feng, Beijing Institute of Technology, China
* Steve Gregory, University of Bristol, UK
* Tongbo Kang, Tsinghua University, China
* Shou-De Lin, National Taiwan University, Taiwan
* Yutao Ma, Wuhan University, China
* Qiang Ma, Yahoo! Inc, USA
* Mohammad Noshad, Harvard University, US
* Jinchang Ren, University of Strathclyde, UK
* Dominik Ślęzak, University of Warsaw, Poland
* Francoise Soulie, Tianjin University, China, France
* Yang Xiang, Tongji University, China
* Erfu Yang, University of Stirling, UK
* Haiqing Yang, Chinese University of Hong Kong, China
* Guangyan Zhang, Tsinghua University, China
* Qi Zhang, Fudan University, China
* Yan Zhang, Peking University, China
* Xiang Zhao, National University of Defense Technology, China

Sponsors
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* National Program on Key Basic Research Project (973 Program), Department of Basic Research, Ministry of Science and Technology, China
* Institute of Computing Technology, Chinese Academy of Sciences, China