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{{Event | {{Event | ||
| − | |Acronym= | + | |Acronym=BigData 2020 |
| − | |Title=IEEE International Conference on Big Data | + | |Title=2020 IEEE International Conference on Big Data |
| + | |Ordinal=8 | ||
| + | |In Event Series=Event Series:IEEE BigData | ||
| + | |Single Day Event=no | ||
| + | |Start Date=2020/12/10 | ||
| + | |End Date=2020/12/13 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=online | ||
| + | |Academic Field=Big Data; Computer Science | ||
| + | |Official Website=http://bigdataieee.org/BigData2020/ | ||
| + | |Submission Link=https://wi-lab.com/cyberchair/2020/bigdata20/scripts/submit.php?subarea=BigD | ||
| + | |DOI=10.25798/4fe0-ky52 | ||
|Type=Conference | |Type=Conference | ||
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|Has coordinator=Yubao Wu | |Has coordinator=Yubao Wu | ||
|has general chair=Srinivas Aluru, Chengxiang Zhai | |has general chair=Srinivas Aluru, Chengxiang Zhai | ||
| − | |has program chair= | + | |has program chair=Chris Jermaine, Xintao Wu, Li Xiong |
|pageCreator=User:Curator 27 | |pageCreator=User:Curator 27 | ||
|pageEditor=User:Curator 27 | |pageEditor=User:Curator 27 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
| − | * Example topics of interest includes but is not limited to the following: | + | {{Event Deadline |
| − | * 1. Big Data Science and Foundations | + | |Notification Deadline=2020/10/16 |
| − | * Novel Theoretical Models for Big Data | + | |Paper Deadline=2020/08/19 |
| − | * New Computational Models for Big Data | + | |Camera-Ready Deadline=2020/11/10 |
| − | * Data and Information Quality for Big Data | + | |Submission Deadline=2020/08/19 |
| − | * New Data Standards | + | }} |
| − | * | + | {{Organizer |
| − | * 2. Big Data Infrastructure | + | |Contributor Type=organization |
| − | * Cloud/Grid/Stream Computing for Big Data | + | |Organization=Institute of Electrical and Electronics Engineers |
| − | * High Performance/Parallel Computing Platforms for Big Data | + | }} |
| − | * Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment | + | {{Event Metric}} |
| − | * Energy-efficient Computing for Big Data | + | {{S Event}} |
| − | * Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data | + | *Example topics of interest includes but is not limited to the following: |
| − | * Software Techniques and Architectures in Cloud/Grid/Stream Computing | + | *1. Big Data Science and Foundations |
| − | * Big Data Open Platforms | + | *Novel Theoretical Models for Big Data |
| − | * New Programming Models for Big Data beyond Hadoop/MapReduce, STORM | + | *New Computational Models for Big Data |
| − | * Software Systems to Support Big Data Computing | + | *Data and Information Quality for Big Data |
| − | * | + | *New Data Standards |
| − | * 3. Big Data Management | + | * |
| − | * Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data | + | *2. Big Data Infrastructure |
| − | * Algorithms and Systems for Big Data Search | + | *Cloud/Grid/Stream Computing for Big Data |
| − | * Distributed, and Peer-to-peer Search | + | *High Performance/Parallel Computing Platforms for Big Data |
| − | * Big Data Search Architectures, Scalability and Efficiency | + | *Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment |
| − | * Data Acquisition, Integration, Cleaning, and Best Practices | + | *Energy-efficient Computing for Big Data |
| − | * Visualization Analytics for Big Data | + | *Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data |
| − | * Computational Modeling and Data Integration | + | *Software Techniques and Architectures in Cloud/Grid/Stream Computing |
| − | * Large-scale Recommendation Systems and Social Media Systems | + | *Big Data Open Platforms |
| − | * Cloud/Grid/Stream Data Mining- Big Velocity Data | + | *New Programming Models for Big Data beyond Hadoop/MapReduce, STORM |
| − | * Link and Graph Mining | + | *Software Systems to Support Big Data Computing |
| − | * Semantic-based Data Mining and Data Pre-processing | + | * |
| − | * Mobility and Big Data | + | *3. Big Data Management |
| − | * Multimedia and Multi-structured Data- Big Variety Data | + | *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 |
| − | * 4. Big Data Search and Mining | + | *Distributed, and Peer-to-peer Search |
| − | * Social Web Search and Mining | + | *Big Data Search Architectures, Scalability and Efficiency |
| − | * Web Search | + | *Data Acquisition, Integration, Cleaning, and Best Practices |
| − | * Algorithms and Systems for Big Data Search | + | *Visualization Analytics for Big Data |
| − | * Distributed, and Peer-to-peer Search | + | *Computational Modeling and Data Integration |
| − | * Big Data Search Architectures, Scalability and Efficiency | + | *Large-scale Recommendation Systems and Social Media Systems |
| − | * Data Acquisition, Integration, Cleaning, and Best Practices | + | *Cloud/Grid/Stream Data Mining- Big Velocity Data |
| − | * Visualization Analytics for Big Data | + | *Link and Graph Mining |
| − | * Computational Modeling and Data Integration | + | *Semantic-based Data Mining and Data Pre-processing |
| − | * Large-scale Recommendation Systems and Social Media Systems | + | *Mobility and Big Data |
| − | * Cloud/Grid/StreamData Mining- Big Velocity Data | + | *Multimedia and Multi-structured Data- Big Variety Data |
| − | * Link and Graph Mining | + | * |
| − | * Semantic-based Data Mining and Data Pre-processing | + | *4. Big Data Search and Mining |
| − | * Mobility and Big Data | + | *Social Web Search and Mining |
| − | * Multimedia and Multi-structured Data-Big Variety Data | + | *Web Search |
| − | * | + | *Algorithms and Systems for Big Data Search |
| − | * 5. Ethics, Privacy and Trust in Big Data Systems | + | *Distributed, and Peer-to-peer Search |
| − | * Techniques and models for fairness and diversity | + | *Big Data Search Architectures, Scalability and Efficiency |
| − | * Experimental studies of fairness, diversity, accountability, and transparency | + | *Data Acquisition, Integration, Cleaning, and Best Practices |
| − | * Techniques and models for transparency and interpretability | + | *Visualization Analytics for Big Data |
| − | * Trade-offs between transparency and privacy | + | *Computational Modeling and Data Integration |
| − | * Intrusion Detection for Gigabit Networks | + | *Large-scale Recommendation Systems and Social Media Systems |
| − | * Anomaly and APT Detection in Very Large Scale Systems | + | *Cloud/Grid/StreamData Mining- Big Velocity Data |
| − | * High Performance Cryptography | + | *Link and Graph Mining |
| − | * Visualizing Large Scale Security Data | + | *Semantic-based Data Mining and Data Pre-processing |
| − | * Threat Detection using Big Data Analytics | + | *Mobility and Big Data |
| − | * Privacy Preserving Big Data Collection/Analytics | + | *Multimedia and Multi-structured Data-Big Variety Data |
| − | * HCI Challenges for Big Data Security & Privacy | + | * |
| − | * Trust management in IoT and other Big Data Systems | + | *5. Ethics, Privacy and Trust in Big Data Systems |
| − | * | + | *Techniques and models for fairness and diversity |
| − | * 6. Hardware/OS Acceleration for Big Data | + | *Experimental studies of fairness, diversity, accountability, and transparency |
| − | * FPGA/CGRA/GPU accelerators for Big Data applications | + | *Techniques and models for transparency and interpretability |
| − | * Operating system support and runtimes for hardware accelerators | + | *Trade-offs between transparency and privacy |
| − | * Programming models and platforms for accelerators | + | *Intrusion Detection for Gigabit Networks |
| − | * Domain-specific and heterogeneous architectures | + | *Anomaly and APT Detection in Very Large Scale Systems |
| − | * Novel system organizations and designs | + | *High Performance Cryptography |
| − | * Computation in memory/storage/network | + | *Visualizing Large Scale Security Data |
| − | * Persistent, non-volatile and emerging memory for Big Data | + | *Threat Detection using Big Data Analytics |
| − | * Operating system support for high-performance network architectures | + | *Privacy Preserving Big Data Collection/Analytics |
| − | * | + | *HCI Challenges for Big Data Security & Privacy |
| − | * 7. Big Data Applications | + | *Trust management in IoT and other Big Data Systems |
| − | * Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication | + | * |
| − | * Big Data Analytics in Small Business Enterprises (SMEs) | + | *6. Hardware/OS Acceleration for Big Data |
| − | * Big Data Analytics in Government, Public Sector and Society in General | + | *FPGA/CGRA/GPU accelerators for Big Data applications |
| − | * Real-life Case Studies of Value Creation through Big Data Analytics | + | *Operating system support and runtimes for hardware accelerators |
| − | * Big Data as a Service | + | *Programming models and platforms for accelerators |
| − | * Big Data Industry Standards | + | *Domain-specific and heterogeneous architectures |
| − | * Experiences with Big Data Project Deployments | + | *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 | ||
* | * | ||
Latest revision as of 17:08, 22 November 2023
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Metrics
Venue
Warning: Venue is missing. The map might not show the exact location.
- 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
