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: 13062   Tracked: 14   Attend: 3

Conference Location
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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