DSAA2017 at TOKYO2017 IEEE International Conference on
Data Science and Advances Analytics
IEEE / ACM
19-21, Oct 2017, Tokyo, JAPANCall for Papers
HIGHLIGHT of DSAA

A very competitive acceptance rate (about 10%) for regular papers, Jointly supported by IEEE, ACM andAmerican Statistical Association, Strong inter-disciplinary and cross-domain culture, Strong engagementof analytics, statistics and industry/government, Double blind, and 10 pages in IEEE 2-column format

Data-driven scientific discovery is regarded as the fourth science paradigm. Datascience is a core driver of the next-generation science, technologies and applications,and is driving new research, innovation, profession, economy and education acrossdifferent disciplines and domains. There are many associated scientific challenges, ranging
from data capture, creation, storage, search, sharing, modeling, analysis, and visualization.
The complication here is not just the storage, I/O, query, and performance, but also theintegration across heterogeneous, interdependent complex data resources for real-timedecision-making, streaming data, collaboration, and ultimately value co-creation. Data
science encompasses the areas of data analytics, machine learning, statistics, optimizationand managing big data, and has become essential to glean understanding from large data
sets and convert data into actionable intelligence. The 2017 IEEE International Conference on
Data Science and Advanced Analytics (DSAA'2017),fully sponsored by IEEE and technicallysponsored by ACM and American Statistical Association, 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. The conference solicitsexperimental and theoretical works on data science and advanced analytics along with theirapplication to real life situations.
General areas of interest to DSAA'2017 include but are not limited to: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 andlearning Structure/group/community/network mining Cloud computing and service data analysis

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 University, Japan
Fosca Giannotti, Inf. Sci. & Tech. Inst. of NRC, Italy
Tomoyuki Higuchi, Inst. Statistical Mathematics, Japan

Program Chairs - Research Track

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

Program Chairs - Application Track

Ying Li, DataSpark Pte. Ltd., Singapore
Rajesh Parekh, Facebook, USASpecial Session Chairs

Topics of InterestFoundations
 Mathematical, probabilistic and statisticalmodels and theories
 Machine learning theories, models andsystems
 Knowledge discovery theories, models andsystems
 Manifold and metric learning Deep learning and deep analysis
 Scalable analysis and learning Non-IID learning Heterogeneous data/information integration
 Data pre-processing, sampling and reduction
 Dimensionality reduction
 Feature selection, transformation andconstruction
 Large scale optimization
 High performance computing for dataanalytics
 Architecture, management and process fordata science

DSAA2017 Web Site

Management, Storage, retrieval and search Cloud architectures and cloud computing
 Data warehouses and Large-scale databases
 Memory, disk and cloud-based storage andanalytics
 Distributed computing and parallel
processing
 High performance computing and processing
 Information and knowledge retrieval, andsemantic search Web/social/databases query and search Personalized search and recommendation
 Human-machine interaction and interfaces Crowdsourcing and collective intelligence
Social issues
 Data science meets social science 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 and social goodApplications
 Best practices and lessons learned from both
success and failure Data-intensive organizations, business andeconomy
 Quality assessment and interestingness
metrics
 Complexity, efficiency and scalability
 Big data representation and visualization
 Business intelligence, data-lakes, big-data
technologies
 Data science education and training practices
and lessons
 Large scale application case studies anddomain-specific applications

Huan Liu, Arizona State University, USA
Albert Bifet, Telecom ParisTech, FranceTrends & Controversy Chairs

Philip S. Yu, University of Illinois at Chicago, USA
Pau-Choo (Julia) Chung, National Cheng Kung Univ., 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
Hang Li, Huawei Technologies, Hong Kong

Publicity Chairs

Tu Bao Ho, Japan Adv. Inst. of Sci. & Tech., Japan
Diane J. Cook, Washington State University, USA
Marzena Kryszkiewicz, Warsaw Univ. of Tech. , Poland

Local Organizing Chairs

Satoshi Kurihara, University of Electro-Commu., Japan
Hiromitsu Hattori, Ritsumeikan University, Japan

Publication Chair

Toshihiro Kamishima, Nat. Inst. of Adv. Ind. Sci. & Tech., JapanWeb Chair

Kozo Ohara, Aoyama Gakuin University, Japan

Sponsorship Chairs

Yoji Kiyota, NEXT Co., Ltd, JapanKiyoshi Izumi, University of Tokyo, JapanTadashi, Yanagihara, KDDI R&D Laboratory, Japan
Longbing Cao, University of Technology Sydney, Australia
Byeong Kang, University of tasmania

Publications

All accepted papers will be published by IEEE andincluded 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.