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DSAA 2017 : The 4th IEEE International Conference on Data Science and Advanced Analytics 2017

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Link: http://www.dslab.it.aoyama.ac.jp/dsaa2017/
 
When Oct 19, 2017 - Oct 21, 2017
Where Tokyo, Japan
Submission Deadline May 25, 2017
Notification Due Jul 25, 2017
Final Version Due Aug 15, 2017
Categories    machine learning   data mining   data science   data analytics
 

Call For Papers

=========================================================================================
CALL For PAPERS

IEEE DSAA'2017: 2017 International Conference on
Data Science and Advanced Analytics

Tokyo, Japan
October 19-21, 2017

http://www.dslab.it.aoyama.ac.jp/dsaa2017/
=========================================================================================


INTRODUCTION

Data driven scientific discovery is an important emerging paradigm for
computing in areas including social computing, services, Internet of
Things, sensor networks, telecommunications, biology, health-care, and
cloud. Under this paradigm, Data Science is the core that drives new
researches in many areas, from environmental to social. There are many
associated scientific challenges, ranging from data capture, creation,
storage, search, sharing, modeling, analysis, and visualization. Among
the complex aspects to be addressed we mention here the integration
across heterogeneous, interdependent complex data resources for
real-time decision making, streaming data, collaboration, and
ultimately value co-creation. Data science encompasses the areas of
data analytics, machine learning, statistics, optimization and
managing big data, and has become essential to glean understanding
from large data sets and convert data into actionable intelligence, be
it data available to enterprises, Government or on the Web.

DSAA takes a strong interdisciplinary approach, features by its strong
engagement with statistics and business, in addition to core areas
including analytics, learning, computing and informatics. DSAA fosters
its unique Trends and Controversies session, Invited Industry Talks
session, Panel discussion, and four keynote speeches from statistics,
business, and analytics. DSAA main tracks maintain a very competitive
acceptance rate (about 10%) for regular papers.

Following the preceeding three editions DSAA'2014 (Shanghai),
DSAA'2015 (Paris), DSAA'2016 (Montreal), the 2017 IEEE International
Conference on Data Science and Advanced Analytics (DSAA'2017) aims to
provide a premier forum that brings together researchers, industry
practitioners, as well as potential users of big data, for discussion
and exchange of ideas on the latest theoretical developments in Data
Science as well as on the best practices for a wide range of
applications.

DSAA is also technically sponsored by ACM through SIGKDD and by the
American Statistics Association.

DSAA'2017 will consist of two main tracks: Research and Applications,
and a series of Special sessions. The Research Track is aimed at
collecting original contributions related to foundations of Data Science
and Data Analytics. The Applications Track is aimed at collecting
original papers (not published nor under consideration at any other
venue) describing substantial contributions related to Data Science and
Data Analytics in real life scenarios. DSAA solicits then both
theoretical and practical works on data science and advanced analytics.
Special sessions replace traditional workshop and are based on call for
proposal. Submission of research on emerging topics is highly
encouraged.


IMPORTANT DATES:

Paper Submission deadline: May 25, 2017
Notification of acceptance: July 25, 2017
Final Camera-ready papers due: August 15, 2017
Early Registration dealine: August 31, 2017


PUBLICATIONS:

All accepted papers will be published by IEEE and will be submitted for
inclusion in the IEEE Xplore Digital Library. The conference proceedings
will be submitted for EI indexing through INSPEC by IEEE. Top quality
papers accepted and presented at the conference will be selected for
extension and invited to the special issues of International Journal of
Data Science and Analytics (Springer).


TOPICS OF INTEREST -- RESEARCH TRACK

General areas of interest to DSAA'2017 include but are not limited to:

1. Foundations

Mathematical, probabilistic and statistical models and theories
Machine learning theories, models and systems
Knowledge discovery theories, models and systems
Manifold and metric learning
Deep learning
Scalable analysis and learning
Non-iidness learning
Heterogeneous data/information integration
Data pre-processing, sampling and reduction
Dimensionality reduction
Feature selection, transformation and construction
Large scale optimization
High performance computing for data analytics
Architecture, management and process for data science


2. Data analytics, machine learning and knowledge discovery

Learning for streaming data
Learning for structured and relational data
Latent semantics and insight learning
Mining multi-source and mixed-source information
Mixed-type and structure data analytics
Cross-media data analytics
Big data visualization, modeling and analytics
Multimedia/stream/text/visual analytics
Relation, coupling, link and graph mining
Personalization analytics and learning
Web/online/social/network mining and learning
Structure/group/community/network mining
Cloud computing and service data analysis


3. Storage, retrieval and search

Data warehouses, cloud architectures
Large-scale databases
Information and knowledge retrieval, and semantic search
Web/social/databases query and search
Personalized search and recommendation
Human-machine interaction and interfaces
Crowdsourcing and collective intelligence


4. 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


TOPICS OF INTEREST -- APLICATIONS TRACK

Papers in this track should motivate, describe and analyze the use of Data
Analytics tools and/or techniques in practical application as well as
illustrate their actual impact.

We seek contributions that address topics such as (but not limited to)
the following:

Best practices and lessons learned from both success and failure
Data-intensive organizations, business and economy
Quality assessment and interestingness metrics
Complexity, efficiency and scalability
Big data representation and visualization
Business intelligence, data-lakes, big-data technologies
Large scale application case studies and domain-specific
applications, such as but not[-1mm] limited to:

Online/social/living/environment data analysis
Mobile analytics for hand-held devices
Anomaly/fraud/exception/change/drift/event/crisis analysis
Large-scale recommender and search systems
Data analytics applications in cognitive systems, planning and decision support
End-user analytics, data visualization, human-in-the-loop, prescriptive analytics
Business/government analytics, such as for financial services,
manufacturing, [-1mm] retail, utilities, telecom, national security,
cyber-security, e-governance, etc.


PAPER SUBMISSION

Submissions to the main conference, including Research Track,
Applications Track, and Special Sessions should be made through the IEEE
DSAA'2017 Submission Web site.

The paper length allowed is a maximum of ten (10) pages, in 2-column US-Letter
style using IEEE Conference template (see the IEEE Proceedings Author Guidelines:
http://www.ieee.org/conferences_events/conferences/publishing/templates.html.
To help ensure correct formatting, please use the style files for
U.S. letter size found at the link above as templates for your
submission, which include both LaTeX and Word.

All submissions will be blind reviewed by the Program Committee on the
basis of technical quality, relevance to conference topics of interest,
originality, significance, and clarity. Author names and affiliations
must not appear in the submissions, and bibliographic references must be
adjusted to preserve author anonymity.


ORGANIZING COMMITTEE

General Chairs:

Hiroshi Motoda, Osaka University, Japan
Fosca Giannotti, Information Science and Technology Institute of the
National Research Council at Pisa, Italy
Tomoyuki Higuchi, Institute of Statistical Mathematics, Japan

Program Chairs -- Research Track

Takashi Washio, Osaka University, Japan
Joao Gama, University of Porto, Portugal

Program Chairs -- Application Track

Ying Li, EV Analysis Corp., also with Jobaline.com, USA
Rajesh Parekh, Facebook, also with KDD2016 and The Hive, USA

Special Session Chairs

Huan Liu, Arizona State University, USA
Albert Bifet, Telecom ParisTech, France

Trends & Controversies Chairs

Philip S. Yu, University of Illinois at Chicago, USA
Pau-Choo (Julia) Chung, National Cheng Kung University, Taiwan

Award Chair

Bamshad Mobasher, DePaul University, USA

Tutorial Chairs

Zhi-Hua Zhou, Nanjing University, China
Vincent Tseng, National Chiao Tung University, Taiwan

Panel Chairs

Geoff Webb, Monash University, Australia
Bart Goethals, University of Antwerp, Belgium

Invited Industry Talk Chairs

Yutaka Matsuo, University of Tokyo, Japan
Hang Li, Huawei Technologies, Hong Kong

Publicity Chairs

Tu Bao Ho, Japan Advanced Institute of Science & Technology, Japan
Diane J. Cook, Washington State University
Marzena Kryszkiewicz, Warsaw University of Technology, Poland

Local Organizing Chairs

Satoshi Kurihara, University of Electro-Communications, Japan
Hiromitsu Hattori, Ritsumeikan University, Japan

Publication Chair

Toshihiro Kamishima, National Institute of Advanced Industrial
Science and Technology, Japan

Web Chair

Kozo Ohara, Aoyama Gakuin University, Japan

Sponsorship Chairs

Yoji Kiyota, NEXT Co., Ltd, Japan
Kiyoshi Izumi, University of Tokyo, Japan
Tadashi, Yanagihara, KDDI Corp., KDDI R\&D Laboratory, Japan


CONTACT INFORMATION

Hiroshi Motoda motoda [AT] ar.sanken.osaka-u.ac.jp
Satoshi Kurihara skurihara [AT] uec.ac.jp

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