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IEEE DSAA'2016: 2016 International Conference on
Data Science and Advanced Analytics

Montreal, Canada
October 17-19, 2016

https://www.ualberta.ca/~dsaa16/
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HIGHLIGHT:

Prof David Donoho and Prof Yoshua Bengio have confirmed to deliver keynotes to DSAA'2016.
In addition to IEEE and ACM, the American Statistics Association also sponsors DSAA'2016.

SUBMISSION WEBSITE:

https://easychair.org/conferences/?conf=dsaa2016

IMPORTANT DATES:

Paper Submission deadline: Friday 20 May, 2016, 11:59 PM PDT
Notification of acceptance: 15 July, 2016
Final Camera-ready papers due: 19 August, 2016

PUBLICATIONS:

All accepted papers will be published by IEEE and included 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 publication in the special issues of some international journals, including International Journal of Data Science and Analytics.

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.

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 analytics. DSAA main tracks maintain a very competitive acceptance rate (about 10%) for regular papers.

Following the previous two successful editions DSAA'2014, DSAA’2015, the 2016 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 and by the American Statistics Association.

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:
1. 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

2. 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
* Behavior, change, dynamics and variation modeling and analytics
* Personalization analytics and learning
* Web/online/social/network mining and learning
* Structure/group/community/network mining
* Cloud computing and service data analysis

3. 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

4. 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, socio-economic activities,
culture, manufacturing, retail, utilities, telecom, national security, cyber-security,
e-governance, etc.