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 2017 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.


The topics of interest include, but are not limited to:

Important Dates

Submissions Due Date: August 7, 2017
Notifications of Acceptance: September 7, 2017
Camera-Ready Deadline: September 15, 2017
Workshop Date: November 18, 2017


The page limit of workshop papers is 8 pages in the standard IEEE 2-column format, including the bibliography and any possible appendices. All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2017 submission guidelines available at: Papers should be submitted in PDF format, electronically, using the CyberChair submission system.

Note that all accepted papers will be included in the IEEE ICDM 2017 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.

Steering Committee

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

Dr. Xiaolong Jin
Institute of Computing Technology, Chinese Academy of Sciences (CAS), China

Dr. Jiafeng Guo
Institute of Computing Technology, Chinese Academy of Sciences (CAS), China

Dr. Huewei Shen
Institute of Computing Technology, Chinese Academy of Sciences (CAS), China

Program Committee



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