2017 IEEE Symposium on Computational Intelligence in Big Data (IEEE CIBD'17)

Building on the success of last year's meeting, the IEEE Symposium Series on Computational Intelligence (SSCI) 2017 will host the Computational Intelligence in Big Data (CIBD) 2017. The event will bring together international experts to discuss theories and applications of big data in computer science. Sponsored by the IEEE Computational Intelligence Society, the symposium will host academics, researchers, professionals, industrial representatives, students and practitioners. Registration to SSCI 2017 will allow participants to attend the CIBD meeting, other sessions, and coffee breaks, lunches and conference banquet.

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Topics

The IEEE CIBD 2017 will bring together international scientists, researchers and professionals to present and discuss the current challenges and opportunities in big data related to computational intelligence (CI). The organisers welcome presentation of recent results relating to CI algorithms, software, systems and architecture, data analytics, current challenges, and new and emerging applications. Presentations relating to industry, novel applications and emerging CI areas in BG are strongly encouraged.
Specific topics include, but are not limited to:

Novel CI methods of big data acquisition
CI in distributed computing of big data
Memory efficient CI algorithms relating to reading, processing or analysing big data
Data mining in big data
Deep learning in big data
Integration of big data, such as multi-modal, multi-fidelity, structured and unstructured data
Big data in industry
Big data in healthcare
Big data and the internet of things
Big data in the future of media and social media
Big data in finances and economy
Big data in public services
Big data in intelligent robotics
Big data driven business or industry
Extracting understanding from distributed, diverse and large-scale data resources
Real time analysis of large data streams
Predictive analysis and in-memory analytics
Dimensionality reduction and analysis of large and complex data
New information infrastructures
Visualisation of big data and visual data analytics
Semantics technologies for big data
Scalable learning in big data
Optimisation of big data in complex systems
Data governance and management
CI in curation of big data
Human-computer interaction and collaboration in big data
Big data and cloud computing
Applications of big data, such as industrial process, business intelligence, healthcare, bioinformatics and security.