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DSAA 2014 : 2014 International Conference on Data Science and Advanced Analytics

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Link: http://datamining.it.uts.edu.au/conferences/dsaa14/
 
When Oct 30, 2014 - Nov 2, 2014
Where Shanghai, China
Submission Deadline Jun 5, 2014
Notification Due Aug 10, 2014
Final Version Due Aug 30, 2014
Categories    data science   data analytics
 

Call For Papers

***** Call for papers ******

2014 International Conference on Data Science and Advanced Analytics
(DSAA 2014)
30 October - 2 November, 2014, Shanghai, China
Website: http://datamining.it.uts.edu.au/conferences/dsaa14/

Important Dates
================

Paper Submission deadline: 5 June, 2014
Notification of acceptance: 10 August, 2014
Final Camera-ready papers due: 30 August, 2014

Submissions
============

https://www.easychair.org/conferences/?conf=dsaa14

Publications
=============

All accepted papers will be published by Springer through LNAI series. Selected top quality papers accepted and presented in the conference for extension and publication in the special issue of some international journals. The accepted workshop papers will be published through Springer CCIS series.

Introduction
=============

Data driven scientific discovery approach has already been agreed to be an important emerging paradigm for computing in areas including social, service, Internet of Things (or sensor networks), and cloud. Under this paradigm, Big Data is the core that drives new researches in many areas, from environmental to social. There are many new scientific challenges when facing this big data phenomenon, ranging from capture, creation, storage, search, sharing, analysis, and visualization. The complication here is not just the storage, I/O, query, and performance, but also the integration across heterogeneous, interdependent complex data resources for real-time decision-making, collaboration, and ultimately value co-creation. Data sciences encompass the larger areas of data analytics, machine learning and managing big data. Data analytics has become essential to glean some understanding from large data sets and convert data into actionable intelligence. With the rapid growth in the volumes of data available to enterprises, Government and on the web, automated techniques for analyzing the data have become essential.
The 2014 International Conference on Data Science and Advanced Analytics (DSAAí2014) aims to provide a timely forum that brings together researchers, industry practitioners, as well as potential users of big data and advanced analytics, to promote collaborations and exchange of ideas and practices, discuss new opportunities, and investigate the best actionable analytics framework for wide range of applications. The conference solicits experimental and theoretical works on data science and advanced analytics along with their application to real life situations.

DSAA is in cooperation with ACM SIGKDD and technically co-sponsored by IEEE Computational Intelligence Society.

Topics of Interest
==================

General areas of interest to DSAAí 2014 include but not Limited to:
Foundations
* New mathematical, probabilistic and statistical models and theories
* New learning theories, models and systems
* Deep analytics and learning
* Distributed and parallel computing (cloud, map-reduce, etc.)
* Non-iidness (heterogeneity & coupling) learning
* Invisible structure, relation and distribution learning
* Intent and insight learning
* Scalable analysis and learning
* Mining multi-source and mixed-source information
* Architecture, management and process
* Data pre-processing, sampling and reduction
* Feature selection and feature transformation
* High performance/parallel/distributed computing
* Analytics architectures and infrastructure
* Heterogeneous data/information integration
* Crowdsourcing
* Human-machine interaction and interfaces

Retrieval, query and search
* Web/social web/distributed search
* Indexing and query processing
* Information and knowledge retrieval
* Personalized search and recommendation
* Query languages and user interfaces

Analytics, discovery and learning
* Mixed-type data
* Mixed-structure data
* Big data modeling and analytics
* Multimedia/stream/text/visual analytics
* Coupling, link and graph mining
* Personalization analytics and learning
* Web/online/network mining and learning
* Structure/group/community/network mining
* Big data visualization analytics
* Large scale optimization

Privacy and security
* Security, trust and risk in big data
* Data integrity, matching and sharing
* Privacy and protection standards and policies
* Privacy preserving big data access/analytics
* Social impact

Evaluation, applications and tools
* Data economy
* Domain-specific applications
* Quality assessment and interestingness metrics
* Complexity, efficiency and scalability
* Anomaly/fraud/exception/change/event/crisis analysis
* Large-scale recommender and search systems
* Big data representation and visualization
* Post-processing and post-mining
* Large Scale Application Case Studies
* Online/business/government data analysis
* Mobile analytics for handheld devices
* Living analytics

Submission Guidelines
======================

A submission of up to 16 pages in the Springer's LNCS format is encouraged. Please follow the Springer's guidelines and technical instructions for the preparation of contributions. (http://www.springer.com/computer/lncs/lncs+authors?SGWID=0-40209-0-0-0)


Advisory Committee
===================

* Usama Fayyad ChoozOn Corporation, USA
* Masaru Kitsuregawa University of Tokyo, Japan
* Rao Kotagiri University of Melbourne, Australia
* Bengchin Ooi National University of Singapore
* Xin Yao University of Birmingham, UK
* Philip S Yu University of Illinois at Chicago, USA

Steering Committee
===================

* Hiroshi Motoda Osaka University and AFOSR/AOARD, Japan
* Geoff Webb Monash University, Australia
* Osmar Zaiane University of Alberta, Canada
* Longbing Cao University of Technology, Sydney, Australia
* Vincent Tseng National Cheng kung University, Taiwan
* Limsoon Wong National University of Singapore
* Herve Martin Laboratoire d'Informatique de Grenoble, France
* Jian Pei Simon Fraser University, Canada
* Kyu-Young Whang Korea Advanced Institute of Science and Technology, Korea
* Diane J. Cook Washington State University
* Charu C. Aggarwal IBM T. J. Watson Research Center, USA
* Bart Goethals University of Antwerp, Belgium


Organizing Committee
=====================

General Chairs
* Philip S Yu University of Illinois at Chicago, USA
* Masaru Kitsuregawa University of Tokyo

Conference Chairs
* Hiroshi Motoda Osaka University and AFOSR/AOARD, Japan
* Bart Goethals University of Antwerp, Belgium
* Minyi Guo Shanghai Jiaotong University, China

Program Committee Chairs
* Longbing Cao University of Technology, Sydney, Australia
* George Karypis University of Minnesota, USA
* Irwin King Chinese University of Hong Kong
* Wei Wang Fudan University, China

Local Arrangement Chairs
* Hongming Cai Shanghai Jiaotong University, China
* Wei Liu University of Technology, Sydney

Workshop Chairs
* Gang Li Deakin University, Australia
* Eric Gaussier UniversitÈ Joseph Fourier, France

Tutorial Chairs
* Junbin Gao Charles Stuart University, Australia
* Sourav S Bhowmick Nanyang Technological University, Singapore

Panel Chairs
* Gabriella Pasi Universit‡ di Milano Bicocca, Italy
* Em-Ping Lim Singapore Management University, Singapore

Sponsorship Chairs
* Guangtao Xue Shanghai Jiaotong University, China

Publicity Chairs
* Xiaohui Tao University of Southern Queensland, Australia
* Xin Wang University of Calgary, Canada
* Xiaodong Yue Shanghai University

Registration/Finance Chairs
* Qi Gu Shanghai Jiaotong University, China

Publication Chair
* Frank Jiang University of Technology, Sydney, Australia

Technical Sponsors
===================

* ACM SIGKDD
* IEEE Computational Intelligence Society
* Springer

Conference organizers
======================

* University of Technology Sydney, Australia
* Shanghai Jiaotong University, China
* Stanford University, USA
* UniversitÈ Joseph Fourier, France

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