IEEE International Conference on Big Data (IEEE BigData 2025)

Topics

• Big Data Infrastructure
  • High Performance/Parallel Computing Platforms for Big Data
  • Cloud/Grid/Stream Computing for Big Data
  • Energy-efficient Computing for Big Data
  • Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
  • Software Techniques and Architectures in Cloud/Grid/Stream Computing
  • Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
  • New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
  • Big Data Open Platforms
  • Software Systems to Support Big Data Computing
• Big Data Science and Foundations
  • New Computational Models for Big Data
  • Novel Theoretical Models for Big Data
  • New Data Standards
  • Data and Information Quality for Big Data
• Big Data Search and Mining
  • Web Search
  • Social Web Search and Mining
  • Distributed, and Peer-to-peer Search
  • Algorithms and Systems for Big Data Search
  • Data Acquisition, Integration, Cleaning, and Best Practices
  • Big Data Search Architectures, Scalability and Efficiency
  • Computational Modeling and Data Integration
  • Visualization Analytics for Big Data
  • Cloud/Grid/StreamData Mining- Big Velocity Data
  • Large-scale Recommendation Systems and Social Media Systems
  • Semantic-based Data Mining and Data Pre-processing
  • Link and Graph Mining
  • Multimedia and Multi-structured Data-Big Variety Data
  • Mobility and Big Data
• Big Data Management
  • Algorithms and Systems for Big Data Search
  • Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
  • Big Data Search Architectures, Scalability and Efficiency
  • Distributed, and Peer-to-peer Search
  • Visualization Analytics for Big Data
  • Data Acquisition, Integration, Cleaning, and Best Practices
  • Large-scale Recommendation Systems and Social Media Systems
  • Computational Modeling and Data Integration
  • Link and Graph Mining
  • Cloud/Grid/Stream Data Mining- Big Velocity Data
  • Mobility and Big Data
  • Semantic-based Data Mining and Data Pre-processing
  • Multimedia and Multi-structured Data- Big Variety Data
• Data Ecosystem
  • Ecosystem services and management
  • Data ecosystem concepts, theory, structure, and process
  • Trust, resilience, privacy, and security issues
  • Methods for data exchange, monetization, and pricing
  • Trust management in Big Data systems
  • Privacy preserving Big Data collection/analytics
  • Experimental studies of fairness, diversity, accountability, and transparency
  • Ecosystem assessment, valuation, and sustainability
• Big Data Learning and Analytics
  • Machine learning algorithms for Big Data
  • Predictive analytics on Big Data
  • Feature representation learning for Big Data
  • Deep learning for Big Data
  • Physics informed Big Data learning
  • Dimension redution for Big Data
• Big Data Applications
  • Big Data Analytics in Small Business Enterprises (SMEs)
  • Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
  • Real-life Case Studies of Value Creation through Big Data Analytics
  • Big Data Analytics in Government, Public Sector and Society in General
  • Big Data Industry Standards
  • Big Data as a Service
  • Experiences with Big Data Project Deployments