Conference Information
DSAA 2017 : International Conference on Data Science and Advanced Analytics
Submission Date:
2017-06-08 Extended
Notification Date:
Conference Date:
Tokyo, Japan
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Conference Location
Call For Papers
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. 
Last updated by Dou Sun in 2017-06-03
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