Conference Information

ISBDAS 2026: International Symposium on Big Data and Applied Statistics

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Submission Date:
2026-02-27
Notification Date:
2026-03-01
Conference Date:
2026-03-06
Location:
Guangzhou, China
Years:
9
Viewed: 17837   Tracked: 5   Attend: 1

Call For Papers

ISBDAS 2026 (International Symposium on Big Data and Applied Statistics) is an academic conference held in Guangzhou, China on 2026-03-06. The paper submission deadline is 2026-02-27. Acceptance notifications are sent on 2026-03-01.

The 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) will be held from March 6 to 8, 2026, in Guangzhou, China. This conference aims to establish a high-level platform for global experts, engineers, researchers, and industry professionals in "Big Data" and "Applied Statistics" to share cutting-edge research and technological innovations, track academic trends, broaden research perspectives, foster in-depth scholarly collaboration, and accelerate industrial partnerships for academic achievements. The topics of interest for submission include, but are not limited to: ◕ Big Data · Big Data Analytics · Models, Architecture, and algorithms of Big Data · Big Data Search and Information Retrieval Techniques · Big Data Acquisition, Integration, Cleaning · Scalable Computing Models, Theories, and Algorithms · Big Data and Deep Learning · Big Data and High Performance Computing · Cyber-Infrastructure for Big Data · Resource Management Approaches for Big Data Systems · Big Data Applications for Internet of Things · Big Data Applications for Smart City · Scalability of Big Data Systems · Big Data Privacy and Security · Big Data Archival and Preservation · Big Data Transformation, and Presentation · Distributed Big Data Storage Architectures · High-Performance Big Data Processing Frameworks · Cloud Native Big Data Computing Models · Lossless Big Data Compression Algorithms · Edge - Cloud Collaborative Big Data Computing ◕ The applied mathematics theory · Statistical Computing in Big Data Environments · Statistical Methods for High-Dimensional Data Analysis · Applications of Nonparametric Statistical Methods in Data Mining · Statistical Learning Theory and Algorithms · Statistical Software & Tool Development · Advanced Cluster Analysis Algorithms · Data Multivariate Statistical Methods · Statistical Data Fusion in Sensor Networks · Statistical Classification Algorithms in Pattern Recognition · Time Series Forecasting & Modeling · Statistical Analysis and Prediction in Power Systems · Statistical Modeling and Optimization in Communication Networks · Statistical Reliability Prediction Algorithms
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