ICDM 2023 WORKSHOP
Incomplete Streaming Data Analysis (ISDA 2023)
December 1,2023,Shanghai, China
The IEEE International Workshop on Incomplete
Streaming Data Analysis (ISDA 2023) will be held in conjunction with the 2023 IEEE
International Conference on Data Mining (ICDM2023)
on December 1-4, 2023.
The
conference venue is planned to be held in Shanghai, China.However,Due to the ongoing COVID-19 circumstances around
the world, ICDM 2023 will be a hybird conference. We will consider to change it to be fully on-site or fully
virtual considering the pandemic situation.
Duration
Half-Day
Scope
With the development of super computing and cloud facilities, massive data are generated at every millisecond,
yielding huge datasets in both laboratory and industry. Rather than storing all the data for post-hoc analysis,
scientists are turning to the paradigm of streaming data analysis: once the data have been generated or
encountered, sophisticated analyses will be performed right there and then. In recent years, streaming data
analyses are highly visible in both machine learning and data science areas and has enjoyed tremendous growth and
exhibited rapid development at both the theorem and application levels.
In many real applications, e.g., online spam filtering, ad click prediction, identifying malicious URLs, and
spatio-temporal signal processing, however, collected streaming data are commonly incomplete due to various
unpredictable factors, e.g., privacy protection, system breakdown, and human errors. Incomplete streaming data
analyses has therefore become a challenge dur to the biases and uncertainties caused by the missing information,
which greatly impairs the learning models' performance.
Aiming at discussing possible solutions to this problem, this workshop proposes to focus on the main theme of
incomplete streaming data analysis (ISDA), which is currently emerging as a rapidly growing field of data science
research. The focus of this workshop is on empirical findings, methodological papers, theoretical underpinnings,
and conceptual insights related to ISDA.
Additionally, an analytical competition is proposed to confront the challenges encountered by industrial applications
when dealing with large-scale graphs that are potentially riddled with noise and incomplete data. Participants are
invited to solve the challenges of community detection and fraudulent group mining through the utilization of innovative
deep graph learning technologies. The primary objective is to harness the power of the emerging pretrained graph neural
networks to surmount the long-standing challenges associated with community detection and fraudulent group mining,
particularly when working with industry-scale graphs. The competition fosters collaboration and innovation among
participants, leading to the creation of cutting-edge system-level and algorithmic techniques that can offer
substantial benefits to the industry.
Topics
This workshop aims at discussing the progress in fundamental principles, practical methodologies, efficient implementations, and applications of ISDA. We expect to encourage an exchange of ideas and perceptions through the workshop, focusing on novel research directions, techniques, and challenges in the areas of ISDA. We welcome papers on topics of interest that include, but are not limited to:
- Incompletestreaming data mining or prediction
- Incremental learning or reinforcement learning in incomplete streaming data situations
- Online learning for incomplete streaming data
- Federal learning for incomplete streaming data
- Machine learning methods for incomplete streaming data
- Concept drift detection, understanding, and adaptation in incomplete streaming data
- Experimental setup and evaluation methods for incomplete streaming data
- Explainable AI methods for incomplete streaming data
- Representation learning on incomplete streaming data
- Representation learning on dynamical graph
- Representation learning on incomplete temporal matrix
- Data mining on incomplete spatio-temporal signal
- Applications of incomplete streaming data in medicine, geology, biology, earth science, and others
Tentative list of invited speaker:
Dr.Xin Luo, Professor, Southwest University, China
- Research group leader of the Graph and Social Computing Lab at the Southwest University
- Various paper awards in top-tier data mining venues, and over 200 refereed publications attracted more than 9,000 citations.
Workshop Chairs with short bio:
Xin Luo received the B.S. degree in computer science from the University of Electronic Science and Technology of China, Chengdu, China, in 2005, and the Ph.D. degree in computer science from the Beihang University, Beijing, China, in 2011. He is currently a Professor of Data Science and Computational Intelligence with the College of Computer and Information Science, Southwest University, Chongqing, China. He has authored or coauthored over 200 papers (including over 100 IEEE Transactions papers) in the areas of big data analysis and graph learning. He is currently serving as a DEiC for IEEE/CAA Journal of Automatica Sinica, and an AE for IEEE Transactions on Neural Networks and Learning Systems. He is a senior member of IEEE.
Di Wu, received his Ph.D. degree from the Chongqing Institute of Green and Intelligent Technology (CIGIT), Chinese Academy of Sciences (CAS), China in 2019 and then joined CIGIT, CAS, China. He is currently a Professor of the College of Computer and Information Science, Southwest University, Chongqing, China. He has more than 60 publications, including 14 IEEE Transactions papers on T-KDE, T-NNLS, T-SC, T-SMC, and T-II, and several conferences papers on ICDM, AAAI, WWW, IJCAI, etc. His research interests include machine learning and data mining. He is serving as an Associate Editor for NEUROCOMPUTING and FRONTIERS IN NEUROROBOTICS.
Shuai Li received the B.E. degree in precision mechanical engineering from Hefei University of Technology, China, in 2005, the M.E. degree in automatic control engineering from University of Science and Technology of China, China, in 2008, and the Ph.D. degree in Electrical and Computer Engineering from Stevens Institute of Technology, Hoboken, NJ, USA, in 2014. He is currently a full professor with Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. His current research interests include dynamic neural networks, robotics, machine learning, and autonomous systems. He is a senior member of IEEE.
Competition Program Chair
- Zhao Li, Professor, Zhejiang University, Zhejiang, China
- Chuntao Hong, Senior Staff Engineer, Ant Group, Beijing, China
- Yongchao Liu, Staff Engineer, Ant Group, Zhejiang, China
Workshop Proposal Co-authors & Tentative Program Committee:
- Yi He, Assistant Professor,Old Dominion University, Virginia, USA
- Zhong Chen, Computational Scientist, Xavier University of Louisiana, Louisiana,USA
- Weibo Liu, Associate Professor, Brunel University London, London, UK
- Song Deng, Professor, University Post & Telecommunication, Jiangsu, China
- Dianlong You, Associate Professor, Yanshan University, Hebei, China
- Ji Xu, Professor, Guizhou University, Guizhou, China
- Ye Yuan, Associate Professor, Southwest University, Chongqing, China
- Heng Lin, Staff Engineer, Ant Group, Beijing, China
- Baokun Wang, Senior Engineer, Ant Group, Shanghai, China
Submission Link:
Contact Information:
Xin Luo, Ph.D.
Professor
College of Computer and Information Science
Southwest University
Room 1305, Building 25, No.2 Tiansheng Road, Beibei District, Chongqing, China
E-mail: luoxin@swu.edu.cn
Webpage:https://scholar.google.com/citations?user=hyGlDs4AAAAJ&hl=zh-CN
Di Wu, Ph.D.
Professor
College of Computer and Information Science
Southwest University
Room 904, Building 25, No.2 Tiansheng Road, Beibei District, Chongqing, China
E-mail: wudi1986@swu.edu.cn
Webpage:https://wudi1989.github.io/Homepage/
Shuai Li, Ph.D.
Professor
Information Technology and Electrical Engineering
University of Oulu
Pentti Kaiteran katu 1, Oulu, Finland
E-mail: shuaili@ieee.org
Webpage:https://scholar.google.com/citations?user=H8UOWqoAAAAJ&hl=zh-CN