WORKSHOP
The First ICDM Workshop on Scalable Streaming Data Mining (SSDM). will be held in
conjunction with
The 2023 IEEE International Conference on Data Mining on December 1-December 4,
The confenrence/workshop venue will be presented in Shanghai, China.
Thank you for your contribution!!
SCOPE
With the rapid development of the Internet, IoT, and AI technology, gigantic volumes of the data stream are generated in real-time at high-speed growth every day. For example, billions of credit card transactions are generated in real-time, which thus incurs heavy computation overhead when detecting credit-card fraud.
Therefore, it is highly desirable to analyze massive data in real-time and mine insightful knowledge, which demands the streaming data mining algorithms, frameworks, and platforms to be fast, lightweight, and scalable to handle the ever-growing streaming data.
This workshop provides a forum for academic researchers and industry practitioners to exchange the most recent progress in novel methods and applications in scalable streaming data mining algorithms and systems.
With ever-growing streaming data in recent years, how to efficiently mine insightful knowledge from streaming data in real-time has broad applications across many domain applications, including the automatic pilot, healthcare, IOT, etc.
Therefore, scalable streaming data mining has recently drawn much attention from multiple research communities, including artificial intelligence, database, networking, and computer system. Many applications demand fast, lightweight, scalable streaming and data mining algorithms, frameworks, and system design.
SSDM 2023 aims to provide a forum for industry and academia to exchange ideas about algorithm design, layout, and applications in the context of scalable streaming data mining.
TOPICS
Today’s streaming data mining approaches still need to overcome many challenges in the face of variable complicated data types, heterogeneous sources, and ever-growing data volumes. Improvement of performance, throughput, scalability, and response time demands more exploration in algorithms, framework, and system design. SSDM 2023 would encourage data mining specialists to develop new theories, algorithms, and applications and welcomes papers that present novel theoretical and practical ideas as well as work making progress from areas including, but not limited to:
New models, theories, and algorithms for streaming data mining
Sketch algorithm designs for streaming data mining
Novel Applications of data streaming mining algorithms, including IoT, web, finance, health care, and other areas.
Scalable, distributed, and parallel streaming mining algorithms and frameworks
Multimodal streaming data mining from heterogeneous sources, including multimedia, graphs, web, and spatio-and-temporal.
Deep learning techniques for streaming data mining
Lightweight streaming data mining algorithms for mobile applications
Personalization and recommendation algorithms based on streaming data
Graph data stream mining algorithm and system design
SUBMISSION & PUBLICATION
Paper submissions should be limited to a maximum of 8
pages, and follow the IEEE ICDM format. More detailed informations are available in the IEEE ICDM 2023 Submission Guidelines.
All accepted papers will be included in the ICDM'23 Workshop Proceedings
published by the IEEE Computer Society Press. Therefore, papers must not
have been accepted for publication elsewhere or be under review for another
workshop, conferences or journals.
All accepted papers, including workshops, must have at least one “FULL” registration. A full registration is either a “member” or “non-member” registration. Student registrations are not considered full registrations. All authors are required to register by 10th October 2023.
IMPORTANT DATE
Submissions due: September 2, 2023
Notifications of Acceptance: September 23, 2023
Camera-ready deadline and copyright forms: October 1, 2023
Workshop day: December 1, 2023
WORKSHOP ORGANIZATION
He Huang is a Professor and Associate Dean of the School of Computer Science and Technology at Soochow University, P. R. China, where he also directs the Institutes of Network Science and Engineering.
He Huang received his Ph.D. from Department of Computer Science and Technology, University of Science and Technology of China. His advisor is Prof. Guoliang Chen.
After graduating from USTC, he joined Soochow University as a lecturer in 2011, and was promoted to associate professor in 2013 and to professor in 2018. From 2019 to 2020, he was a visiting research scholar with Florida University, Gainesville, United States.
He is the recipient of the Changjiang (Yangtze River) Youth Scholar award in 2021. He published 100+ peer-reviewed journal/conference papers and has 13 Chinese patents. He is a senior member of IEEE and a member of the Association for Computing Machinery (ACM).
His research interests include traffic measurement for high-speed Internet, Software Defined Networks, mobile computing, privacy and security, cyber-physical systems, and algorithms. He has been supported by China NSF, with the total support of more than 3.0 million CNY.
His students and he won three best paper awards (Bigcom 2016, IEEE MSN 2018, IEEE Bigcom 2018). He is currently serving as an associate editor of 2 international journals, including Internet of Things and Cyber-Physical Systems Journal, Frontiers in the Internet of Things.
He served at various capacities (publicity chair, publication chair, and technical program committee) in a number of conferences, e.g., Publicity Co-Chair of ACM MobiHoc-MSCC 2016, Publication Chair of the Second IEEE INFOCOM Workshop on Networking Algorithms (WNA) in conjunction with IEEE INFOCOM 2022, TPC member of IEEE INFOCOM, IEEE MASS, IEEE ICC, and IEEE Globecom.
Siqiang Luo is Nanyang Assistant Professor at the School of Computer Science and Engineering, Nanyang Technological University.
He is also affiliated with DANTE. He received his B.S. and M.S. degrees in computer science from Fudan University, and a Ph.D. degree in computer science from the University of Hong Kong.
His research interest lies in efficient and effective designs for big data. His research interest lies in improving scalability and efficacy during big data processing.
He regularly publishes papers in top-tier conferences such as SIGMOD, PVLDB, ICDE, WWW and ICML. 
Shigang Chen received his M.S. and Ph.D. degrees in Computer Science from University of Illinois at Urbana-Champaign in 1996 and 1999, respectively.
Prior to that, he received his B.S. degree in Computer Science from University of Science and Technology of China in 1993. After graduating from UIUC, he worked with Cisco Systems on network security for three years and helped start a network security company, Protego Networks.
He joined University of Florida as an assistant professor in 2002, and was promoted to associate professor in 2008 and to professor in 2013. He was a recipient of IEEE Communications Society Best Tutorial Paper Award in 1999, NSF CAREER Award in 2007, and Cisco University Research Award in 2007, 2012. He published 200+ peer-reviewed journal/conference papers and has 13 US patents.
He held University of Florida Research Foundation Professorship in 2017-2020 and University of Florida Term Professorship in 2017-2020. He is an IEEE Fellow and an ACM Distinguished Member.
He is currently serving as an associate editor of IEEE Transactions on Mobile Computing and served as editor of IEEE/ACM TON, IEEE TVT, and CN. He also served at various capacities in a number of conferences, e.g., Area TPC chair for IEEE INFOCOM 2014-2022, General Chair of IEEE BIGCOM 2019, and Co-Chair of NSF Workshop on Edge Networking 2018.CONTACT US
Haipeng Dai
Nanjing University
CS Building 313, Nanjing University, 163 Xianlin Blvd. Nanjing, Jiangsu 210023, China
Email: haipengdai@nju.edu.cn
Meng Li
Nanjing University
Email: menson@smail.nju.edu.cn