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

• Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data

• Algorithms and Systems for Big Data Search

• Distributed, and Peer-to-peer Search

• Big Data Search Architectures, Scalability and Efficiency

• Data Acquisition, Integration, Cleaning, and Best Practices

• Visualization Analytics for Big Data

• Computational Modeling and Data Integration

• Large-scale Recommendation Systems and Social Media Systems

• Cloud/Grid/Stream Data Mining- Big Velocity Data

• Link and Graph Mining

• Semantic-based Data Mining and Data Pre-processing

• Mobility and Big Data

• Multimedia and Multi-structured Data- Big Variety 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

• Data Acquisition, Integration, Cleaning, and Best Practices

• Visualization Analytics for Big Data

• Computational Modeling and Data Integration

• Large-scale Recommendation Systems and Social Media Systems

• Cloud/Grid/StreamData Mining- Big Velocity Data

• Link and Graph Mining

• Semantic-based Data Mining and Data Pre-processing

• Mobility and Big Data

• Multimedia and Multi-structured Data-Big Variety 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

6. Ethics, Privacy and Trust in Big Data Systems

• Techniques and models for fairness and diversity

• Experimental studies of fairness, diversity, accountability, and transparency

• Techniques and models for transparency and interpretability

• Trade-offs between transparency and privacy

• Intrusion Detection for Gigabit Networks

• Anomaly and APT Detection in Very Large Scale Systems

• High Performance Cryptography

• Visualizing Large Scale Security Data

• Threat Detection using Big Data Analytics

• Privacy Preserving Big Data Collection/Analytics

• HCI Challenges for Big Data Security & Privacy

• Trust management in IoT and other Big Data Systems

7. 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