In the big data era, with the enrichment of data collection and description measures, a wide array of data in various formats are collected much easier than before. It is significant to discover the knowledge hidden in the mass by comprehensive understanding and learning to realize the data intelligence, which can help human in various dimensions, such as intelligent decisions and predictive services. However, the volume, heterogeneous, low-quality and multimodal characteristics of the collected data pose great challenges to the design of knowledge discovery methods. Therefore, this workshop aims to provide a forum to present the state-of-the-art advancements on knowledge discovery for big data, which include related surveys, algorithms, platforms, systems and applications.

Call for Papers pdf

The topics include but not limited to:
  • Acquisition, transmission, storage, index and visualization for big data
  • Innovative methods for big data analytics
  • Multimodal data fusion
  • Cross-modal reasoning and retrieval
  • Domain adaption and transfer learning
  • Zero-shot and few-shot learning
  • Deep learning and reinforcement learning
  • Graph learning
  • Knowledge graph reasoning and completion
  • Recommendation system
  • Knowledge tracing
  • Multiscale big data computing
  • Biomedical big data computing
  • Bioinformatics quantitative structure-activity relationship model
  • Natural language processing
  • Parallel, accelerated, and distributed algorithms and frameworks for big data
  • Security, privacy and trust in big data
  • Big data in Internet of Things
  • Methods for academic, traffic, medical, financial, social media and judicial big data
  • Other methods, models, architectures and applications related to big data


  • Paper submission deadline: June 1, 2022
  • Acceptance Notification: July 1, 2022
  • Paper Submission

    Authors are invited to submit their original research work that has not previously been submitted or published in any other venue. Regular, work-in-progress (WiP), workshop/special session, and poster papers need to be submitted via Easychair: ( link )

    Papers should be prepared in IEEE CS Proceedings format. IEEE formatting information: ( link )

    All accepted papers will be published in an IEEE Computer Society proceedings (IEEE-DL and EI indexed). Best Paper Awards will be presented to high quality papers. Selected papers will be recommended to prestigious journal Special Issues.

    Some papers originally submitted as full papers can be accepted as short papers or posters during the review process. In such cases, the authors will need to reduce the paper accordingly when preparing the camera-ready version. At least one of the authors of any accepted paper is requested to register and present the paper at the conference. Ps: Log on the system and choose the Track of International Workshop on Knowledge Discovery for Big Data.

    Our special issue "Few-Shot Learning for Knowledge Engineering and Intellectual System" is online now. You can check the information at:

    Selected papers will be invited to submit an extended version to
    1.Mathematics ( SI: Recent Advances in Artificial Intelligence and Machine Learning )
    2.Electronics ( SI: Big Data Analysis Based Network ).


  • Organizing Chair
  • Ruixin Ma, Dalian University of Technology, China

  • Organizing Co-Chair
  • Xu Yuan, Dalian University of Technology, China

    Suhua Zhang´╝îUniversity of British Columbia´╝îCanada

    Haozhe Wang, University of Exeter, UK

  • Program Committee Chair
  • Liang Zhao, Dalian University of Technology, China

    Xiaodi Huang, Charles Sturt University, Australia

    Yujia Zhu, University of Exeter, UK

    Shuo Yu, King Abdullah University of Science and Technology, SA

    Fangming Zhong, Dalian University of Technology, China

  • International Committee Chair
  • Shi Chen, The Hong Kong University of Science and Technology, China

    Xu Zhang, Simon Fraser University, Canada

    Han Xu, University of Exeter, UK

  • Advisory Chair
  • Zhikui Chen, Dalian University of Technology, China


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  • Dr. Ruixin Ma
  • Dr. Fangming Zhong
  • Dr. Liang Zhao
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