IEEE BigData 2019

IEEE BigData 2019
IEEE International Conference on Big Data
Event in series	IEEE BigData
Dates	2019/12/09 (iCal) - 2019/12/12
Homepage:	http://bigdataieee.org/BigData2019/
Location
Location:	Los Angeles, California, USA
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Committees
General chairs:	Roger Barga, Carlo Zaniolo
Table of Contents

 Example topics of interest includes but is not limited to the following:

• 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
• 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
• Big Data Management
• Search and Mining of a variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
• Algorithms and Systems for Big DataSearch
• 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
• 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
• 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
• 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
• 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