Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. With the advances of information communication technologies, it is critical to improve the efficiency and accuracy of modern data processing techniques. The past decade has witnessed the tremendous technical advances in Sensor Networks, Internet/Web of Things, Cloud Computing, Mobile/Embedded Computing, Spatial/Temporal Data Processing, and Big Data, and these technologies have provided new opportunities and solutions to data processing techniques. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity).
This workshop wants to demonstrate the emerging issues in the research of Big Data and approaches towards the knowledge engineering. Original and research articles are solicited in all aspects including theoretical studies, practical applications, and experimental prototypes.
All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. Potential topics include, but are not limited to:
* Big data novel theory, algorithm and applications * Big data standards * Big data mining and analytics * Big data Infrastructure, MapReduce and Cloud Computing * Big data visualization * Big data semantics, scientific discovery and intelligence * Big data performance analysis and large-scale deployment * Knowledge Acquisition with big data * Knowledge-Based and Expert Systems with big data * Knowledge Representation and Retrieval with big data * Knowledge Engineering Tools and Techniques with big data * Time and Knowledge Management Tools with big data * Knowledge Visualization with big dataPapers must be written in English. An electronic version (Postscript, pdf, or MS Word format) of the full paper should be submitted using the following URL: https://www.easychair.org/conferences/?conf=seke2016 (submission website will be open after January 1, 2016). Please use Internet Explorer as the browser. Manuscript must include a 200-word abstract and no more than 6 pages of 2-column formatted Manuscript for Conference Proceedings (include figures and references but exclude copyright form).
Some best ranked papers from this workshop will be selected for a super-sized special issue of the International Journal of Software Engineering and Knowledge Engineering (IJSEKE) to be published in December 2016 for early dissemination.
Notification of acceptance: April 20, 2016
Early registration deadline: May 10, 2016
Camera-ready copy: May 10, 2016
PC Chair:
Zheng Xu, Tsinghua University, China
PC Co-Chairs:
Yunhuai Liu, Hong Kong University of Science and Technology, Hong Kong
Neil Yen, Aizu University, Japan
Kim-Kwang Raymond Choo, University of South Australia, Australia
Vijayan Sugumaran, Oakland University, USA
Workshop Program Committee:
Shunxiang Zhang, Anhui Univ. of Sci. & Tech., China
Guangli Zhu, Anhui Univ. of Sci. & Tech., China
Tao Liao, Anhui Univ. of Sci. & Tech., China
Xiaobo Yin, Anhui Univ. of Sci. & Tech., China
Xiangfeng Luo, Shanghai Univ., China
Xiao Wei, Shainghai Univ., China
Kun Gao, Zhejiang Wanli Institute, China
Huan Du, Shanghai Univ., China
Zhiguo Yan, Fudan University, China
For enquiries, please contact xuzheng@shu.edu.cn.