Computational Intelligence - February 2017 - Cover4

DSAA2017 at TOKYO	
2017 IEEE International Conference on
Data Science and Advances Analytics	
IEEE / ACM	
19-21, Oct 2017, Tokyo, JAPAN	

　 Call	
  for	
  Papers　　　　　　　　　　　　　　　　　　　	
　　　　　　　　　　　　　　　　　　　	

D	

ata driven scientific discovery approach has already been agreed to be an
important emerging paradigm for computing in areas including social, service,
Big	
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 realtime 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 a deep understanding of large data sets
and to 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 2017 International Conference on
Data Science and Advanced Analytics (DSAA'2017), fully sponsored by IEEE and
technically sponsored by ACM, aims to provide a premier forum that brings together
researchers, industry practitioners, as well as potential users of data science, 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.	

Topics of Interest
General areas of interest to DSAA'2017 include but are not limited to:
Foundations
Ø  Mathematical, probabilistic and statistical
models and theories
Ø  Machine learning theories, models and
systems
Ø  Knowledge discovery theories, models and
systems
Ø  Manifold and metric learning
Ø  Deep learning
Ø  Scalable analysis and learning
Ø  Non-IIDness learning
Ø  Heterogeneous data/information
integration
Ø  Data pre-processing, sampling and
reduction
Ø  Dimensionality reduction
Ø  Feature selection, transformation and
construction
Ø  Large scale optimization
Ø  High performance computing for data
analytics
Ø  Architecture, management and process for
data science
Data analytics, machine learning and knowledge
discovery
Ø  Learning for streaming data
Ø  Learning for structured and relational data
Ø  Latent semantics and insight learning
Ø  Mining multi-source and mixed-source
information
Ø  Mixed-type and structure data analytics
Ø  Cross-media data analytics
Ø  Big data visualization, modeling and
analytics
Ø  Multimedia/stream/text/visual analytics
Ø  Relation, coupling, link and graph mining
Ø  Personalization analytics and learning

Digital Object Identifier 10.1109/MCI.2016.2627704

Ø  Web/online/social/network mining and
learning
Ø  Structure/group/community/network mining
Ø  Cloud computing and service data analysis
Storage, retrieval and search
Ø  Data warehouses, cloud architectures
Ø  Large-scale databases
Ø  Information and knowledge retrieval, and
semantic search
Ø  Web/social/databases query and search
Ø  Personalized search and recommendation
Ø  Human-machine interaction and interfaces
Ø  Crowdsourcing and collective intelligence
Privacy and security
Ø  Security, trust and risk in big data
Ø  Data integrity, matching and sharing
Ø  Privacy and protection standards and
policies
Ø  Privacy preserving big data access/analytics
Ø  Social impact	
	
  
Applications
Ø  Best practices and lessons learned from
both success and failureData integrity,
matching and sharing
Ø  Data-intensive organizations, business and
economyPrivacy preserving big data access/
analytics
Ø  Quality assessment and interestingness
metrics
Ø  Complexity, efficiency and scalability
Ø  Big data representation and visualization
Ø  Business intelligence, data-lakes, big-data
technologies
Ø  Large scale application case studies and
domain-specific applications

DSAA2017 Web Site
http://www.dslab.it.aoyama.ac.jp/dsaa2017/
Key Dates
*  Special sessions proposal:
*  Paper Submission:
*  Notiﬁcation of acceptance:
*  Camera-Ready:
*  Early Registration:

March 31, 2017
May 25, 2017
July 25, 2017
Aug. 15, 2017
Aug. 31, 2017

General Chairs:

Hiroshi Motoda, Osaka Univ., Japan
Fosca Giannotti, ISTI, Italy
Tomoyuki Higuchi, ISM, 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, USA

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

Trends & Controversy Chairs

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

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, The Univ. of Tokyo, Japan
Hang Li, Huawei Technologies, Hong Kong

Publicity Chairs
Tu Bao Ho, JAIST, Japan
Diane J. Cook, Washington State University, USA
Marzena Kryszkiewicz, Warsaw Univ. of Tech. , Poland

Local Organizing Chairs
Satoshi Kurihara, The Univ. of Electro-Commu., Japan
Toshihiro Kamishima, AIST, Japan
Kozo Ohara, Aoyama Gakuin University, Japan

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

Publications
All accepted papers will be published by IEEE and
included in the IEEE Xplore Digital Library. The
conference proceedings will be submitted for EI indexing
through INSPEC by IEEE. Accepted Long presentation
papers will be invited to Int. J. Data Science and Analytics,
Springer.
http://www.dslab.it.aoyama.ac.jp/dsaa2017/ http://www.Jobaline.com
Table of Contents for the Digital Edition of Computational Intelligence - February 2017
