会议信息

ISBDAS 2027: International Symposium on Big Data and Applied Statistics

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截稿日期:
2026-12-28 还有 67 天
通知日期:
2027-01-26
会议日期:
2027-03-12
会议地点:
Nanjing, China
届数:
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浏览: 19240   关注: 5   参加: 1
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ISBDAS
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会伴指数 (CP-I)

51.8 / 100
全站第 1,294 名 / 共 5,759 个会议 · 前 23%

数学与自然科学 第 8 / 71 数据挖掘与数据库 第 65 / 339

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
65
社区关注 (10%)
32
资料公开度 (15%)
55

用到的输入: 有据可查的届次：10 · 在会伴关注它的研究者：5 人 · 过去 24 个月打开过本页的研究者：6 人

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置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-10-22

征稿

ISBDAS 2027 (International Symposium on Big Data and Applied Statistics) is an academic conference held in Nanjing, China on 2027-03-12. The paper submission deadline is 2026-12-28. Acceptance notifications are sent on 2027-01-26.

Track 1: Big Data Technologies and Applications Big Data Analytics Models, Architecture, and Algorithms of Big Data Big Data Search and Information Retrieval Big Data Acquisition, Integration, and Cleaning Scalable Computing Models and Algorithms Big Data and Deep Learning Big Data and High Performance Computing Cyber-Infrastructure for Big Data Resource Management in Big Data Systems IoT Applications of Big Data Smart City Applications of Big Data Big Data Privacy and Security Distributed Big Data Storage Architectures Cloud-Native Big Data Computing Models Edge-Cloud Collaborative Big Data Computing Track 2: Applied Statistics and Intelligent Computing Statistical Computing in Big Data Environments Statistical Methods for High-Dimensional Data Nonparametric Statistical Methods in Data Mining Statistical Learning Theory and Algorithms Multivariate Statistical Methods Time Series Forecasting and Modeling Advanced Cluster Analysis Algorithms Statistical Data Fusion in Sensor Networks Statistical Classification in Pattern Recognition Statistical Analysis and Prediction in Power Systems Statistical Modeling and Optimization in Communication Networks Statistical Reliability Prediction Algorithms Applied Mathematics and Optimization Statistical Applications in Engineering Statistical Software and Tool Development
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