In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data.

• The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://bigdataieee.org/BigData2013/) and the regular paper acceptance rate is 17.0%.
• The IEEE Big Data 2022 ( http://bigdataieee.org/BigData2022/ , regular paper acceptance rate: 19.2%) was held in Osaka, Japan, Dec 17-20, 2022 with close to 1250 registered participants from 54 countries.
• The IEEE Big Data 2023 (http://bigdataieee.org/BigData2023/ , regular paper acceptance rate: 17.4%) was held in Sorrento, Italy, Dec 15-18, 2023 with close to 950 registered participants from 50 countries.

The 2024 IEEE International Conference on Big Data (IEEE BigData 2024) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications.

We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Washington DC USA this year. Example topics of interest includes but is not limited to the following:

1. Big Data Science and Foundations

• Novel Theoretical Models for Big Data
• New Computational Models for Big Data
• Data and Information Quality for Big Data
• New Data Standards

2. Big Data Infrastructure

• Cloud/Grid/Stream Computing for Big Data
• High Performance/Parallel Computing Platforms for Big Data
• Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
• Energy-efficient Computing for Big Data
• Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
• Software Techniques and Architectures in Cloud/Grid/Stream Computing
• Big Data Open Platforms
• New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
• Software Systems to Support Big Data Computing

3. Big Data Management

• Data Acquisition, Integration, Cleaning, and Best Practices
• Computational Modeling and Data Integration
• Large-scale Recommendation Systems and Social Media Systems
• Cloud/Grid/Stream Data Mining- Big Velocity Data
• Mobility and Big Data
• Multimedia and Multi-structured Data- Big Variety Data
• Compliance and Governance for Big Data

4. Big Data Search and Mining

• Social Web Search and Mining
• Web Search
• Algorithms and Systems for Big Data Search
• Distributed, and Peer-to-peer Search
• Big Data Search Architectures, Scalability and Efficiency
• Link and Graph Mining
• Semantic-based Data Mining and Data Pre-processing
• Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data

5. Big Data Learning and Analytics

• Predictive analytics on Big Data
• Machine learning algorithms for Big Data
• Deep learning for Big Data
• Feature representation learning for Big Data
• Dimension redution for Big Data
• Physics informed Big Data learning
• Visualization Analytics for Big Data

6. Data Ecosystem

• Data ecosystem concepts, theory, structure, and process
• Ecosystem services and management
• Methods for data exchange, monetization, and pricing
• Trust, resilience, privacy, and security issues
• Privacy preserving Big Data collection/analytics
• Trust management in Big Data systems
• Ecosystem assessment, valuation, and sustainability
• Experimental studies of fairness, diversity, accountability, and transparency

7. Foundation Models for Big Data

• Big data management for pre-training
• Big data management for fine-tuning
• Big data management for prompt-tuning
• Prompt Engineering and its Management
• Foundation Model Operationalization for multiple users

8. Big Data Applications

• Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
• Big Data Analytics in Small Business Enterprises (SMEs)
• Big Data Analytics in Government, Public Sector and Society in General
• Real-life Case Studies of Value Creation through Big Data Analytics
• Big Data as a Service
• Big Data Industry Standards
• Experiences with Big Data Project Deployments