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IEEE BigData 2026: IEEE International Conference on Big Data

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IEEE BigData
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Submission Date:
2026-08-21
Notification Date:
2026-10-24
Conference Date:
2026-12-14
Location:
Phoenix, Arizona, USA
CCF: C   ICORE: B   Viewed: 54742   Tracked: 20   Attend: 6

Conference Partner Index (CP-I)

56.6 / 100
Ranked #718 of 5,645 conferences · Top 13%

#38 of 336 in Data Mining & Databases

Academic recognition (35%)
72
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
61
Public record completeness (15%)
35

Inputs used: Listed as CCF C, ICORE B · Researchers following it here: 20 · Researchers who opened this page in the past 24 months: 71

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · 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 60% - 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 2026-09-01

Call For Papers

IEEE BigData 2026 (IEEE International Conference on Big Data) is a CCF C / ICORE B conference held in Phoenix, Arizona, USA on 2026-12-14. The paper submission deadline is 2026-08-21. Acceptance notifications are sent on 2026-10-24.

Topics of Interest: Big Data Science and Foundations New Data Standards Data and Information Quality for Big Data New Computational Models for Big Data Novel Theoretical Models for Big Data Big Data Infrastructure Software Systems to Support Big Data Computing New Programming Models for Big Data beyond Hadoop /MapReduce, STORM Big Data Open Platforms Software Techniques and Architectures in Cloud /Grid /Stream Computing Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data Energy-efficient Computing for Big Data Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment High Performance /Parallel Computing Platforms for Big Data Cloud /Grid /Stream Computing for Big Data Big Data Management Compliance and Governance for Big Data Multimedia and Multi-structured Data- Big Variety Data Mobility and Big Data Cloud/Grid/Stream Data Mining- Big Velocity Data Large-scale Recommendation Systems and Social Media Systems Computational Modeling and Data Integration Data Acquisition, Integration, Cleaning, and Best Practices Big Data Search and Mining Search and Mining of a variety of data, multimedia data. social, sensor /IoT /IoE, and including scientific and engineering, Data Ecosystem Experimental studies of fairness, diversity, accountability, and transparency Ecosystem assessment, valuation, and sustainability Trust management in Big Data systems Privacy preserving Big Data collection/analytics Trust, resilience, privacy, and security issues Methods for data exchange, monetization, and pricing Ecosystem services and management Data concepts, theory, structure, and process Foundation Models for Big Data Foundation Model Operationalization for multiple users Prompt Engineering and its Management Big data management for prompt-tuning Big data management for fine-tuning Big data management for pre-training Big Data Applications Complex Big Data Domain Applications in Engineering Human Resources Cybersecurity Industrial IoT Media and Entertainment Telecommunication Advertising Marketing Supply Chains Retailing Transportation Logistics Education Law Business Finance Urban Planning Smart Cities Big Data for Science Science Knowledge Foundation Models AI-accelerated Simulations and Modeling AI-Ready Scientific Big Data Systems and Analysis Medicine and Health Science Chemical Engineering and Synthetic Biology Materials Informatics Geospatial and Planetary Analytics Climate and Earth Sciences Genomics and Bioinformatics, Structural Biology Science Computational Astrophysics AI-Driven Scientific Discovery in Physical Sciences Big Data Benchmarks Responsible Dataset Development Advanced Collection and Curation Practices Data-Centric AI Methods, Tools, and Systems Data Generators and Synthetic Environments Benchmarking Tools and Platforms Benchmarks and Evaluation Frameworks New Datasets and Collections Big Data BlueSky Ideas Novel Interdisciplinary Synthesis Paradigm-Shifting Applications Foundational Assumption Challenges New Algorithmic Opportunities Complex and Hard Problem Solving Trending, Emerging, Bold, or Visionary Concepts
Last updated by Dou Sun on

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