KDBD 2022: The 4th International Workshop on Knowledge Discovery for Big Data
http://www.ubinec.org/~kdbd2022/
in conjunction with
The 20th IEEE International Conference on Pervasive Intelligence and Computing (PICom 2022)
August 23-26 2022, Calgary, Canada
INTRODACTION
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
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
IMPORTANT DATES
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 in the main tracks, workshops, special sessions and demos/posters 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.
Selected papers will be invited to submit an extended version to Mathematics and Computer Science ( SI: Recent Advances in Artificial Intelligence and Machine Learning )or Networks ( SI: Big Data Analysis Based Network ).
COMMITTEES
Organizing Chair:
Ruixin Ma, Dalian University of Technology, China
Organizing Co-Chair:
Xu Yuan, Dalian University of Technology, China
Suhua Zhang, University of British Columbia, Canada
Haozhe Wang, University of Exeter, UK
Program Committee Chair:
Xiaodi huang Charles Sturt University Australian
Xiaochen Li, University of Luxembourg, Luxembourg
Yujia Zhu, University of Exeter, UK
Shuo Yu, King Abdullah University of Science and Technology, SA
Zhehuan Zhao, Dalian University of Technology,China
International Committee Chair:
Shi Chen, The Hong Kong University of Science and Technology, China
Yi Yang, Beihang University, China
Yonglin Leng, Bohai University, China
Advisory Chair:
Zhikui Chen, Dalian University of Technology, China