Conference Information
BigData Congress 2019: International Congress on Big Data
http://conferences.computer.org/bigdatacongress/2019/Submission Date: |
2019-03-02 Extended |
Notification Date: |
2019-04-15 |
Conference Date: |
2019-07-08 |
Location: |
Milan, Italy |
Years: |
8 |
Viewed: 21844 Tracked: 14 Attend: 3
Call For Papers
2019 IEEE International Congress on Big Data aims to provide an international forum for researchers and practitioners in academia and industry to present latest research findings and practice on major and emerging topics in Big Data. Big data is frequently described in terms of the four V's: volume, velocity, variety, and veracity. In this forum we encourage submissions that cover foundational advances in these four areas. We additionally seek papers that study the interplay of Big Data in the science and industry ecosystem, including infrastructure and frameworks, HPC, AI, convergence research, reproducibility, and data papers. Technical topics of interest include but are not limited to the following:
Models and Algorithms
Models and algorithms for big data analytics
Models and algorithms for big data driven AI
Data Systems Engineering
From campus to cloud for big data analytics
Cyberinfrastructure for big data
AI and high performance computing for big data
Big Data Management
Data quality and integrity
Provenance and workflows
Security and Privacy
System security and integrity
Privacy-preserving architectures and analytics
Usability / threat tradeoffs
Service Performance
Big data service performance evaluation
Big data application benchmarks
Big data service reliability and availability
Real-time big data services
Optimization and Visualization
Optimization algorithms and systems with big data
Visual analytics with big data
Social media data mining
Case Studies and Experiences
Big data for enterprise, government, and society
Big data for science and engineering research
Architectures and policies for preservation of scientific data
Case studies of value creation with open data
Data papers for data sets
Last updated by Dou Sun in 2019-02-09
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