Conference Information

ISBDAS 2027: International Symposium on Big Data and Applied Statistics

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ISBDAS
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Submission Date:
2026-12-28 Due in 103 days
Notification Date:
2027-01-26
Conference Date:
2027-03-12
Location:
Nanjing, China
Years:
Viewed: 18859   Tracked: 5   Attend: 1

Conference Partner Index (CP-I)

51.8 / 100
Ranked #1,218 of 5,682 conferences · Top 22%

#8 of 69 in Mathematics & Physical Sciences #62 of 337 in Data Mining & Databases

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
65
Community attention (10%)
32
Public record completeness (15%)
55

Inputs used: Editions on record: 10 · Researchers following it here: 5 · Researchers who opened this page in the past 24 months: 6

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-16

Call For Papers

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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