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FADS 2017 : International Conference on the Frontiers and Advances in Data Science


When Oct 23, 2017 - Oct 25, 2017
Where Xi'an, China
Submission Deadline Jul 10, 2017
Notification Due Aug 20, 2017
Final Version Due Sep 1, 2017
Categories    big data   data mining   data analysis   data modeling

Call For Papers

Our lives and society have been transformed by advances in modern information and communication technologies. Devices such as mobile phones and tablets and applications such as social networks have changed how we interact with the world around us and each other and the way we conduct business, government and science. With computational technologies permeating almost all aspects of human activity the amount of data being generated is constantly increasing and we are surrounded by a wealth of new forms of data. We are at the cusp of a new data and information revolution.
This proliferation of data calls for novel, multi and interdisciplinary approaches in data science and analytics to tackle the problems that data pose. New approaches for harnessing data and drawing insights will bring huge benefits to fields as diverse as health, finance, running smarter cities, the environment, business and public policy.
The First International Conference on Frontiers and Advances in Data Science and Analytics (FAADS) will bring together scientists, professionals, industry practitioners and users from range of disciplinary backgrounds and application domains to share knowledge and the latest developments in data science and analytics.
We invite the submission of original and previously unpublished theoretical and practical work in all fields of data science and analytics including methodologies and techniques for big data. All submissions will be reviewed by at least two members of the Program Committee on the basis of novelty, technical quality, relevance to the conference theme, significance, and clarity of presentation. Accepted papers will be submitted for inclusion into IEEE Xplore.

FADS welcomes submissions on (but not limited to) the following topics:
Data Science Foundations
Machine Learning
Mathematical and statistical models
Novel theoretical models
Computational Models
Preprocessing and dimensionality reduction
Efficiency and complexity
Multi-stream reasoning and analytics
Text analysis and mining
Causal inference
Modelling complex and big data
Personalisation analytics and recommender systems
Social network analytics
Multimedia/image processing and analytics
Information retrieval and search
Semantic information extraction and reasoning
Data Infrastructure and Management
New data standards
Data cleansing
Data integration
Data sharing
Data linkage
Data curation and publishing
Cloud/Grid/Stream Computing architectures
Distributed and parallel/high performance processing
Data warehouses
Open platforms for analytics
Big data architectures and platforms
Security, governance and privacy
Intrusion, anomaly and threat detection
Data integrity
Data security and risk
Trust and trust management
Privacy preserving techniques and anonymisation
Privacy protection standards and policies
Legal aspects of analytics and big data
Social and economic aspects
Ethical considerations in the era of analytics and big data
New business models
Sociological aspects of analytics and big data
Analytics and big data for sustainable development
Analytics and big data for the social good
Scientific applications of analytics and big data
Internet of Things
Internet of Persons
Smart Cities and Transport
Business and Finance Analytics
Healthcare analytics and decision support
Decision making and support systems
Analytics and big data for policy making and the public sector
Social networks and applications
Industrial applications of analytics and big data
Analytics for telecommunications and networks applications

Important Dates
Paper Submission deadline: 10th July 2017
Notification of acceptance: 20th August 2017
Camera-ready Manuscript: 1st September 2017
Registration deadline: (for accepted papers): 10th September 2017

The conference is technically co-sponsored by the Institute for Analytics and Data Science — University of Essex, UK & RI Computer Chapter, Northwest University, and IEEE Xi’an Section.

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