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 different 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 2015 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:


Paper submissions should be limited to a maximum of 10 pages, and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2015 Submission Instructions Please submit your manuscript through the Cyberchair Submission System

Note that all accepted papers will be included in the ICDM'15 Workshop Proceedings published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

Important Dates

Submissions Due: July 20, 2015
Notifications of Acceptance: September 1, 2015
Workshop Day: November 13, 2015

Workshop Chairs

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

A/Prof. Rafal A. Angryk
Georgia State University

Program Committee




Dr. Xiaoqian Sun
Institute of Computing Technology, Chinese Academy of Sciences

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