Conference Information
DataCom 2019: International Conference on Big Data Intelligence and Computing
https://www.cs.ccu.edu.tw/~conference/datacom2019/
Submission Date:
2019-08-05 Extended
Notification Date:
2019-08-31
Conference Date:
2019-11-18
Location:
Kaohsiung, Taiwan
Years:
5
Viewed: 8330   Tracked: 10   Attend: 7

Conference Location
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Call For Papers
   Big data is a rapidly expanding research area spanning the fields of computer science and information management, and has become a ubiquitous term in understanding and solving complex problems in different disciplinary fields such as engineering, applied mathematics, medicine, computational biology, healthcare, social networks, finance, business, government, education, transportation and telecommunications.

    The goal of the IEEE 2019 International Conference on Big Data Intelligence and Computing (IEEE DataCom 2019) is to establish an international forum for engineers and scientists to present their ideas and experiences in the fields of Big Data intelligence and computing. IEEE DataCom 2019 welcomes paper submissions on innovative work from researchers in academia, industry and government describing original research work in Big Data.

     IEEE DataCom 2019 will be held on November 18-21, 2019, co-located with IEEE SOCA 2019, IEEE SC2 2019, IOV 2019 in Kaohsiung, Taiwan

Scope and Topics

Topics of interest include, but are not limited to:

    The 5Vs of the data landscape: volume, variety, velocity, veracity, value
    Big data science and foundations, analytics, visualization and semantics
    Software and tools for big data management
    Security, privacy and legal issues specific to big data
    Big data economy, QoS and business models
    Scientific discovery and business intelligence
    Software, hardware and algorithm co-design, high-performance computing
    Large-scale recommendation systems and graph analysis
    Infrastructures and systems for big data analytics and managements
    Middleware and tools for big data analytics and managements
    Algorithmic, experimental, prototyping and implementation
    Data quality issues: such as validation, metrics, optimizations and consistency
    Data-driven innovation, computational modelling and data integration
    Data intensive computing theorems and technologies
    Big data for advanced manufacturing and productivity
    Modelling, simulation and performance evaluation
    Green data centers / environmental-friendly perspectives
    Computing, scheduling and resource management for sustainability
    Complex applications in areas where massive data is generated
Last updated by Dou Sun in 2019-07-21
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