Conference Information

BigData Congress 2019: International Congress on Big Data

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Submission Date:
2019-03-02 Extended
Notification Date:
2019-04-15
Conference Date:
2019-07-08
Location:
Milan, Italy
Years:
8
Viewed: 25878   Tracked: 14   Attend: 3

Call For Papers

BigData Congress 2019 (International Congress on Big Data) is an academic conference held in Milan, Italy on 2019-07-08. The paper submission deadline is 2019-03-02 (extended). Acceptance notifications are sent on 2019-04-15.

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
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