CFP SDM 2010 Workshop on High Performance Analytics

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 15th, 2010 Notification of acceptance: February 1st, 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 griscom@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)