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

Call for Papers
Submission Website
Submissions to the main conference, including Research Track and Applications Track are available from Easy Chair (https://easychair.org/conferences/?conf=dsaa2017).

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:
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-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

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