Difference between revisions of "IEEE BigData 2020"
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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 | ||
| + | * | ||
Revision as of 15:14, 23 June 2020
| IEEE BigData 2020 | |
|---|---|
| Event in series | IEEE BigData |
| Dates | 2020/12/10 (iCal) - 2020/12/13 |
| Homepage: | http://bigdataieee.org/BigData2020/ |
| Location | |
| Location: | Atlanta, Gorgia, USA |
Loading map... | |
| Table of Contents | |
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The following coordinate was not recognized: Geocoding failed.
- 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
Facts about "IEEE BigData 2020"
| Acronym | IEEE BigData 2020 + |
| End date | December 13, 2020 + |
| Event in series | IEEE BigData + |
| Event type | Conference + |
| Has location city | Atlanta + |
| Has location country | Category:USA + |
| Has location state | Gorgia + |
| Homepage | http://bigdataieee.org/BigData2020/ + |
| IsA | Event + |
| Start date | December 10, 2020 + |
