“Big Data and Stream Analytics” Workshop
@ SIAM Conference on Data Mining 2015

Vancouver, British Columbia, Canada.
May 2nd, 2015
https://sites.google.com/site/sdm2015bsa


In the era of massive data being produced every day, both academia and industry show great enthusiasm about the field “big data”. Evolving for almost a decade, however, big data is no longer just about managing and processing terabytes or even petabytes of data. Instead recent research and applications of big data analytics focus more on accelerating and improving the decision making process.
Among different forms of big data, stream data is the most prevalent one, ranging from website click, impression data, and Internet traffic to sensors/devices’ continuously collected input signals, anomaly detection in enterprise network perimeters. On the other hand, stream data analytics faces some unique challenges in the setting of big data, and its research is still less treaded with tremendous opportunities.
In industry, the trend of Internet of Things (IoT) has been obvious and has become the one of the new hotbeds in stream data analytics from the increasing rate of generated data from the sensor applications, communication networks, click-streams and so many other sources. Real-time analysis of these fast big data streams suffers from extra computational and mining challenges compared to the traditional big data analytics.
This workshop aims to present and share new research in defining and showcasing the value of big data analytics. Specifically we welcome papers on addressing, but not limited to, the aforementioned challenges in stream data analytics. We welcome new theory, innovation, industrial applications, new distributed computing paradigm, or big data analytics tools. We also want to forge the synergy of big data analytics between industry and academia.

Topics of interest includes but not limited to:
• Online Machine Learning and Data Stream Mining
• Adaptive data mining techniques and machine learning in data streams
• Learning from time Series and non-stationary data stream
• Streaming data processing architecture
• Distributed data stream models
• Graph and Geometry Problems in the Stream Model
• Function Approximation in Data Streams
• Knowledge base construction and adaption in Data Streams
• Visualization and analytical techniques for big data streams
• Theoretical and computational models for IoT big data
• Real-Time and Real-World Applications using Stream data
• Unsupervised Feature Leaning and Deep learning on stream data


Paper Submission:
Publication:
All workshop accepted papers will be included as part of the proceeding of SIAM Conference on Data Mining 2015. Workshop paper should follow the format of SIAM conference which can be found at: http://www.siam.org/tex/books/booktex.php. Workshop paper should not exceed maximally 6 pages.


Important Dates:
• Workshop: May 2nd, 2015


Workshop Organizers:
Chunsheng (Victor) Fang, Senior Data Scientist, EMC/Pivotal, Palo Alto, CA
Qian (Jane) You, Research Scientist, Amazon.com, Seattle, WA,
Huan Wang, Research Scientist, Yahoo.com, Adjunct Professor at New York University, NY

Workshop Program Committee:
Anca Ralescu, Professor of EECS department, College of Engineering, University of Cincinnati
Daisy Zhe Zhang, Assistant Professor of the CISE department, University of Florida
Mahdi Azarafrooz, Data Scientist and Software Engineer, Pivotal
Xia Ning, Assistant Professor of at the Department of Computer & Information Science, Indiana University - Purdure University Indianapolis.
Yuandong Tian, Researcher in Google X, Self-driving Car team