Scope
With the fast development of information technologies, big data has emerged from various applications of many areas. Extracting knowledge and fusion analysis from big data have become important but challenging tasks. Though a plenty of theoretical theories and novel techniques have been proposed, the following challenges are still unsolved. First, extracted knowledge from multiple dimension could be different and fusing knowledge is not addressed currently. Second, knowledge varies by spatio-temporal dynamics; however, existing knowledge management theories (such as database and knowledge graph) cannot handle them. Third, the proportion of valuable data is small with the increasing amount of big data, intelligent analysis methods are urgently in need. Hence, new methods and theories are needed to associate data from multiple dimensions and to analyze from big data more intelligently.
This workshop, collocated with the 6th IEEE International Conference on Data Science in Cyberspace (IEEE DSC2021), will bring big data researchers together to exchange their ideas, innovations, and novel methods for multi-dimensional data association and intelligent analysis. Research from various domains, including but not limited to knowledge representation, knowledge management, data fusion and association, big data mining, knowledge verification, big data applications are highly appreciated. Attendees are invited to introduce their latest research results on multi-dimensional data association and intelligent analysis theories/innovations/algorithms and how these innovations can be applied in real-world applications.WORKSHOP AREAS
Topic interest include but not limited to:
1. Knowledge
representation theory and method
2. Data association
methods from multiple dimension
3. Knowledge
management theory and method
4. Big data analysis
of multiple dimension
5. Data
fusion/association theory and method
6. Intelligent
analysis method
7. Data/knowledge
verification
8. Application in
Internet of Things
9. Application in
cyber attack and defense
10. Application in
intelligence analysis
11. Research challenges on
data analysis and knowledge management
12. Entity linking
algorithms
13. Linking prediction in
knowledge graphs
PAPER SUBMISSION
All submissions should be written in English and submitted via our submission system: https://cmt3.research.microsoft.com/MDATA2021. A paper submitted to MDATA 2021 cannot be under review for any other conference or journal during the entire period that it is considered for MDATA 2021, and must be substantially different from any previously published work. Submissions are reviewed in a single-blind manner. Please note that all submissions must strictly adhere to the IEEE templates as provided below. The templates also act as a guideline regarding formatting. In particular, all submissions must use either the LATEX template or the MS-Word template. Please follow exactly the instructions below to ensure that your submission can ultimately be included in the proceedings. If you have any question on MDATA 2021, please feel free to contact zqgu@gzhu.edu.cn or liaiping@nudt.edu.cn.
IMPORTANT DATES
Acceptance notification:
Camera-ready copy:
Conference Date: October 9-11, 2021
ORGANIZATION
General Chair
General Co-Chairs
Program Committee
Hongkui Tu,National University of Defense Technology, Hunan, China
Jing Qiu, Peng Cheng Laboratory, Shenzhen, China
Shudong Li, Guangzhou University, Guangdong, China
Mohan Li, Guangzhou University, Guangdong, China
Keke Tang, Guangzhou University, Guangdong, China
Qi Xuan, Guangzhou University, Zhejiang, China
Yizhi Ren, Hangzhou Dianzi University, Zhejiang, China
Zhijun Li, Harbin Institute of Technology, Heilongjiang, China
Denghui Zhang, Guangzhou University, Guangdong, China
