********************** KDBD -2021 CFP ********************** The 3rd International Workshop on Knowledge Discovery for Big Data (KDBD 2021) http://ubinec.org/~kdbd2021 in conjunction with the 19th IEEE International Conference on Pervasive Intelligence and Computing (PICom 2021) http://cyber-science.org/2021/picom/ August 23-26 2021, Calgary, Canada

 COMMITTEES  INTRODUCTION

Organizing Chair In the big data era, with the enrichment of data collection and Bo Xu, Dalian of , description measures, a wide array of data in various formats are Organizing Co-Chair collected much easier than before. It is significant to discover the Liang Zhao, of Technology, China Qingchen Zhang, Hainan University, China knowledge hidden in the mass by comprehensive understanding Haozhe Wang, University of Exeter, UK and learning to realize the data intelligence, which can help Program Committee Chair human in various dimensions, such as intelligent decisions and Fangming Zhong, Dalian University of Technology, China Xiaochen Li, University of Luxembourg, Luxembourg predictive services. However, the volume, heterogeneous, low- Yujia Zhu, University of Exeter, UK quality and multimodal characteristics of the collected data pose Shuo Yu, King Abdullah University of and Technology, SA great challenges to the design of knowledge discovery methods. Xu Zhang, The University of British Columbia,Canada Therefore, this workshop aims to provide a forum to present the International Committee Chair Shi Chen, Emporia State University, USA state-of-the-art advancements on knowledge discovery for big Yi Yang, Beihang University, China data, which include related surveys, algorithms, platforms, Yonglin Leng, Bohai University, China systems and applications. Advisory Chair Zhikui Chen, Dalian University of Technology, China

 SCOPE AND TOPICS

• Acquisition, transmission, storage, index and • Natural language processing visualization for big data • Parallel, accelerated, and distributed algorithms and • Innovative methods for big data analytics frameworks for big data • Multimodal data fusion • Security, privacy and trust in big data • Cross-modal reasoning and retrieval • Big data in Internet of Things • Domain adaption and transfer learning • Methods for academic, traffic, medical, financial, social • Zero-shot and few-shot learning media and judicial big data • Deep learning and reinforcement learning • Other methods, models, architectures and applications • Graph Learning and knowledge graphs related to big data

 IMPORTANT DATES  PAPER SUBMISSION GUIDELINE

All acceptedPaper Submission papers will be Deadline: published byMay IEEE 1, 202(IEEE1 -DL and Papers should be prepared in IEEE CS format within 6 pages. EI indexed)Acceptance in Conference Notification: Proceedings. June 1, 2021 IEEE formatting information: (link) Selected Camera papers-ready will be Submission: invited to submit June an 2extended0, 2021 version Authors are invited to submit their original research work using to Scientific Programming（SI: Scientific Programming for IEEE CS Proceedings format via PICOM 2021 EDAS Multimodal Big Data ） or Frontiers in Neurorobotics(SI: (https://edas.info/N26908). Ps: Log on the system and choose Privacy-preserving Deep Heterogeneous View Perception for the Track of International Workshop on Knowledge Discovery Data Learning). for Big Data.