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Metrics
Venue
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- 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
- 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. 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
- 6. Hardware/OS Acceleration for Big Data
- FPGA/CGRA/GPU accelerators for Big Data applications
- Operating system support and runtimes for hardware accelerators
- Programming models and platforms for accelerators
- Domain-specific and heterogeneous architectures
- Novel system organizations and designs
- Computation in memory/storage/network
- Persistent, non-volatile and emerging memory for Big Data
- Operating system support for high-performance network architectures
- 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
