IEEE BigData 2020

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IEEE BigData 2020
IEEE International Conference on Big Data
Event in series IEEE BigData
Dates 2020/12/10 (iCal) - 2020/12/13
Homepage: http://bigdataieee.org/BigData2020/
Submitting link: https://wi-lab.com/cyberchair/2020/bigdata20/scripts/submit.php?subarea=BigD
Location
Location: Atlanta, Gorgia, Online
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Important dates
Papers: 2020/08/19
Submissions: 2020/08/19
Notification: 2020/10/16
Camera ready due: 2020/11/10
Committees
Organizers: Yubao Wu
General chairs: Srinivas Aluru, Chengxiang Zhai
PC chairs: Chris Jermaine, Xintao Wu, Li Xiong
Table of Contents


Event

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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