Saturday 26 November 2016

Call for Papers: IEEE DSAA'17


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

Tokyo, Japan
October 19-21, 2017


  • A very competitive acceptance rate (about 10%) for regular papers
  • Jointly supported by IEEE, ACM and American Statistics Association
  • Strong inter-disciplinary and cross-domain culture
  • Strong engagement of 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. Data science is a core driver of the next-generation science,  technologies and applications, and is driving new researches, innovation,  profession, economy and education  across disciplines and across domains. 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, society, 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 data science. DSAA main tracks maintain a very competitive acceptance rate (about 10%) for regular papers.

Following the preceeding three editions DSAA'2016 (Montreal),  DSAA'2015 (Paris), and DSAA'2014 (Shanghai), 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 solicits then both theoretical and practical works on data science and advanced analytics. 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 (unpublished nor under consideration at any other venue) and significant contributions related to foundations of Data Science and Analytics. The Applications Track is aimed at collecting original papers describing better and reproduciable practices with substantial contributions to Data Science and Analytics in real life scenarios. DSAA special sessions substantially upgrade traditional workshops to encourage emerging topics in data science while maintain regirous selection criteria. Call for proposals to organize special sessions are highly encouraged.


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


All accepted papers, including main tracks and special sessions, 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 (JDSA, Springer).


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 and deep analytics
  • Scalable analysis and learning
  • Non-IID 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. Management, storage, retrieval and search

  • Cloud architectures and cloud computing
  • Data warehouses and large-scale databases
  • Memory, disk and cloud-based storage and analytics
  • Distributed computing and parallel processing
  • High performance computing and processing
  • 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. 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 good


Papers in this track should motivate, describe and analyze the reproduciable use of Data science tools and/or techniques in practical applications as well as illustrate their actual impact on business and/or society.

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
  • Data science education and training practices and lessons
  • Large scale application case studies and domain-specific applications, such as:
    • 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, retail, utilities, telecom, national security, cyber-security, e-governance, etc.


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 the IEEE 2-column format (see the IEEE Proceedings Author Guidelines:

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.


Call for tutorials:
Call for special sessions:
Call for sponsorship:


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,                  DataSpark Pte. Ltd., Singapore
  • 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
NGDS (Next Generation Data Scientist) Award Chairs
  • Kenji Yamanishi,          University of Tokyo, Japan
  • Xin Wang,                 University of Calgary, Canada
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
  • Longbing Cao,             University of Technology Sydney, Australia  
  • Byeong Kang               University of Tasmania, Australia


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