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    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

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