Conference Information
DSAA 2016 : International Conference on Data Science and Advanced Analytics
https://www.ualberta.ca/~dsaa16/
Submission Date:
2016-05-20
Notification Date:
2016-07-15
Conference Date:
2016-10-17
Location:
Montreal, Canada
Years:
3
Viewed: 3046   Tracked: 3   Attend: 0

Conference Location
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Call For Papers
Publications

Conference content will be submitted for inclusion into IEEE 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 publication in the special issues of some international journals, including IEEE TKDE, ACM TKDD, ACM TIIS and WWWJ.

Introduction

Data driven scientific discovery is an important emerging paradigm for computing in areas including social computing, services, Internet of Things, sensor networks, telecommunications, biology, health-care, and cloud. Under this paradigm, Data Science is the core that drives new researches in many areas, from environmental to social. 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, Government or on the Web.

Following the previous two successful editions DSAA'2014, DSAA’2015, the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA’2016) 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.

DSAA'2016 will consist of two main tracks: Research and Applications. The Research Track is aimed at collecting original contributions related to foundations of Data Science and Data Analytics. The Applications Track is aimed at collecting original papers (not published nor under consideration at any other venue) describing substantial contributions related to Data Science and Data Analytics in real life scenarios. DSAA solicits then both theoretical and practical works on data science and advanced analytics.

Topics of Interest -- Research Track

General areas of interest to DSAA'2016 include but are not limited to:

    Foundations
        New mathematical, probabilistic and statistical models and theories
        New machine learning theories, models and systems
        New knowledge discovery theories, models and systems
        Manifold and metric learning, deep learning
        Scalable analysis and learning
        Non-iidness learning
        Heterogeneous data/information integration
        Data pre-processing, sampling and reduction
        High dimensional data, feature selection and feature transformation
        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
        Intent 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 
    Storage, retrieval and search
        Data warehouses, cloud architectures
        Large-scale databases
        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 
    Privacy and security
        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 

Topics of Interest -- Applications Track

Papers in this track should motivate, describe and analyse the use Data Analytics tools and/or techniques in practical application as well as illustrate their actual impact.

We seek contributions that address topics such as (but not limited to) the following:

    Best practices and lessons
    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
    Large scale application case studies and domain-specific applications, such as but not limited to:
        Online/social/living/environment data analysis
        Mobile analytics for hand-held devices
        Anomaly/fraud/exception/change/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. 
Last updated by Dou Sun in 2016-04-10
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