Due to multiple requests, we have extended the deadline for the SIAM Data
Mining 2010 Workshop on High Performance Analytics to January 29th, 2010.
----------------------------CALL FOR PAPERS----------------------------
SIAM Data Mining 2010 Workshop on High Performance Analytics
Algorithms, Implementations, and Applications
Co-located with the SIAM International Conference on Data Mining
April 29 -- May 1, 2010
Columbus, Ohio
The Columbus, A Renaissance Hotel
http://sites.google.com/site/workshophpa/
Objectives:
With advances in data collection and storage technologies, large data
sources have become ubiquitous. Today, organizations routinely collect
terabytes of data on a daily basis with the intent of gleaning non-trivial
insights on their business processes. To benefit from these advances, it is
imperative that data mining and machine learning techniques scale to such
proportions. Such scaling can be achieved through the design of new and
faster algorithms and/or through the employment of parallelism.
Furthermore, it is important to note that emerging and future processor
architectures (like multi-cores) will rely on user-specified parallelism to
provide any performance gains. Unfortunately, achieving such scaling is
non-trivial and only a handful of research efforts in the data mining and
machine learning communities have attempted to address these scales.
At the other end of the spectrum, the past few years have witnessed the
emergence of several platforms for the implementation and deployment of
large-scale analytics. Examples of such platforms include Hadoop (Apache)
and Dryad (Microsoft). These platforms have been developed by the
large-scale distributed processing community and can not only simplify
implementation but also support execution on the cloud making large-scale
machine learning and data mining both affordable and available to all.
Today, there is a large gap between the data mining/machine learning and
the large scale distributed processing communities. To make advances in
large-scale analytics it is imperative that both these communities work
hand-in-hand.
The objectives of the high performance analytics workshop are as follows.
• Characterize the state of the high performance analytics arena
• Promote algorithm design for high performance data mining/machine
learning on the terabyte scale
• Identify large-scale data mining/machine learning problems by
studying applications
• Identify infrastructure/programming model requirements to implement
large scale data mining/machine learning
• Bring together researchers in high performance data mining/machine
learning and large scale distributed data processing
Topics of Interest:
• Application case studies that showcase the need for large scale
machine learning/data mining in business, science, engineering, and other
domains
• Parallel and distributed algorithms for large scale machine
learning/data mining
• Exploiting modern and specialized hardware such as multi-core
processors, GPUs, STI Cell processor, etc
• Memory hierarchy aware data mining/machine learning algorithms
• Streaming data algorithms for machine learning and data mining
• New platforms and/or programming model proposals for
parallel/distributed machine learning and data mining for batch and/or
stream domains
• Evaluation of platforms (such as Hadoop) and/or programming models
(such as map-reduce) for batch and/or stream domains
Submission dates and guidelines:
Submission deadline: January 29th, 2010
Notification of acceptance: February 8th, 2010
Final papers due: February 12th, 2010
All papers accepted should have a maximum length of 10 pages
(single-spaced, 2 column, 10 point font, and at least 1" margin on each
side). Authors should use US Letter (8.5" x 11") paper size. Papers must
have an abstract with a maximum of 300 words and a keyword list with no
more than 6 keywords. Authors are required to submit their papers
electronically in PDF format by email to whpa.chairs (at) gmail.com.
We would like to encourage you to prepare your paper in LaTeX2e. Papers
should be formatted using the SIAM SODA macro, which is available through
the SIAM website. You can access it at
http://www.siam.org/books/authors/p_handbook8.php. The filename is
soda2e.all. Make sure you use the macros for SODA and Data Mining
Proceedings; papers prepared using other proceedings macros will not be
accepted.
For Microsoft Word users, please convert your document to the PDF format.
If you need information about the formats for preparing the paper using
Word, you may contact Nancy Griscom at gris...@siam.org.
All submissions should clearly present the author information including the
names of the authors, the affiliations and the emails.
Organization:
Workshop Co-chairs:
• Amol Ghoting (IBM T. J. Watson Research Center)
• Rong Yan (Facebook)
• Xifeng Yan (University of California at Santa Barbara)
PC Members (confimed):
• Srinivasan Parthasarathy (Ohio State University)
• Alexander Gray (Georgia Tech)
• Yuan Yu (Microsoft Research)
• Anthony Nguyen (Intel Research)
• Philip Yu (University of Illinois at Chicago)
• Edwin Pednault (IBM Research)
• Jimeng Sun (IBM Research)
• Tamara Kolda (Sandia National Laboratories)
• Hong Tang (Yahoo!)
• Jie Tang (Tsinghua University)
• Vipin Kumar (University of Minnesota)
• Jerry Zhao (Google)
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