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BigData 2017 : The International Symposium on Big Data and Smart Sustainable Society (BigData-2017)

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Link: http://www2.docm.mmu.ac.uk/STAFF/L.Han/BigData-2017/
 
When Jun 21, 2017 - Jun 23, 2017
Where Exeter, UK
Submission Deadline Mar 15, 2017
Notification Due Apr 22, 2017
Final Version Due May 15, 2017
 

Call For Papers

Call for Papers

The International Symposium on Big Data and Smart Sustainable Society (Bigdata-2017)

To be held in conjunction with The 10th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2017) (http://cse.stfx.ca/~CPSCom2017/call4paper.php)
21-23 June, Exeter, UK


Accepted papers will be included in the IEEE conference proceedings published by IEEE Computer Society Press (indexed by EI). Distinguished selected papers will be published in Special Issues of International Journals indexed by SCI.

1. Introduction
By 2020, the total size of digital data generated by social networks, sensors, biomedical imaging and simulation devices, will reach an estimated 44 Zettabytes (e.g. 44 trillion gigabytes) according to IDC report. We are now in the era of “big data”. This type of “big” data, together with the advances in information and communication technologies such as Internet of things (IoT), connected smart objects, wearable technology, ubiquitous computing, is transforming every aspect of modern life and bringing great challenges and spectacular opportunities to fulfill our dream of a sustainable smart society.

This symposium aims to provide a platform and forum to discuss and report current state-of-the-art, new solutions, future directions to address challenges, issues and success stories on how to harness potential advances in the digital area to improve people’s life (e.g. big data, IoT, mobile/ ubiquitous computing), and how to maximize the use of big data processing and data analytics to realize the smart society.

2. The topics include but are not limited to:

--Novel intelligent system architectures for big data processing and data analytics (e.g. parallel and distributed computing/Cloud computing; distributed algorithms)
--Novel data analytics/machine learning algorithms for efficient big data analytics (e.g. probabilistic and statistical models and methods for learning from streaming data, feature extraction/sample reduction, deep learning, etc.)
--Ubiquitous computing/wearable technology/ Internet of things (e.g. mobile computing, IoT architecture, fog computing)
--Security and privacy in big data (e.g. privacy preserving big data analytics)
--Big data driven applications in different domains such as health, smart cities, social science, energy, bioscience, security, transportation, agriculture etc.

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