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


When Oct 19, 2017 - Oct 21, 2017
Where Tokyo, Japan
Submission Deadline Jun 8, 2017
Notification Due Jul 25, 2017
Final Version Due Aug 15, 2017
Categories    foundations   data analytics, ml   data management   social issues

Call For Papers

Call for Papers
Submission Website
Submissions to the main conference, including Research Track and Applications Track are available from Easy Chair (

Important Dates

Special sessions proposal: March 31 February 25, 2017
Paper Submission: May 25 June 8, 2017 (PDT) (extended)
Notification of acceptance: July 25, 2017
Camera-Ready: Aug 15, 2017
Advanced Registration: Aug. 31, 2017

Highlights of DSAA

A very competitive acceptance rate (about 10%) for regular papers
Jointly supported by IEEE, ACM and American Statistical 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 preceding 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 Statistical 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 reproducible 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 rigorous selection criteria. Call for proposals to organize special sessions are highly encouraged.

Topics of Interest — Research Track
General areas of interest to DSAA’2017 include but are not limited to:

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-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

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

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

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

Topics of Interest — Applications Track

Papers in this track should motivate, describe and analyze the reproducible 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.

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).
Papers Formatting

The paper length allowed is a maximum of ten (10) pages, in 2-column U.S. letter style using IEEE Conference template (see the IEEE Proceedings Author Guidelines:

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.
LaTeX and Word Templates for Conference Papers
To help ensure correct formatting, please use the style files for U.S. letter size found at the link below as templates for your submission. These include LaTeX and Word: Violations of any of the above paper specifications may result in rejection of your paper. Please note that the Latex template does not allow for keywords. If you are using the Latex template, do not include keywords in your paper.
Contact for inquiries about Paper submission
If you have any inquiry about the paper submission, please contact either of the following chairs of the track you intend to submit a paper.

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