Multi-dimensional Data Association and inTelligent Analysis (MDATA 2022)

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 DSC2022), 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/MDATA2022. A paper submitted to MDATA 2022 cannot be under review for any other conference or journal during the entire period that it is considered for MDATA 2022, 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 2022, please feel free to contact zqgu@gzhu.edu.cn or liaiping@nudt.edu.cn.


IMPORTANT DATES

Full paper due: April 30, 2022
Acceptance notification: June 18, 2022
Camera-ready copy: June 26, 2022
Conference Date: July 11-13, 2022

ORGANIZATION

General Chair

Zhaoquan Gu Guangzhou University, Guangdong, China

General Co-Chairs

Aiping Li National University of Defense Technology, Hunan, China
Weihong Han Peng Cheng Laboratory, Shenzhen, China

Program Committee

Ye Wang, National University of Defense Technology, Hunan, China
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



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