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Call for Papers
Third Workshop on Large-scale Data Mining: Theory and Applications (LDMTA 2011)
in conjunction with SIGKDD2011, August 21-24, 2011, San Diego, CA, USA
http://www.arnetminer.org/LDMTA2011
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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 intent of this workshop is to further research efforts on large-scale data mining and to encourage researchers and practitioners to share their studies and experiences on the implementation and deployment of scalable data mining and machine learning algorithms.


Topics of Interest

* Application case studies that showcase the need for large-scale machine learning/data mining. Areas of interest of interest include financial modeling, web mining, medical informatics, climate modeling, and mining retail and e-commerce data.
* Parallel and distributed algorithms for large-scale machine learning/data mining, data preprocessing, and cleaning.
* 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.
* Performance studies comparing cloud, grid, and cluster implementations
* Data intensive computing approaches
* Future research challenges in cloud and data intensive computing

Important dates and guidelines

Submission deadline: May 7th, 2011
Notification of acceptance: June 3rd, 2011
Final papers due: June 15th, 2011

All papers submitted should have a maximum length of 8 pages and must be prepared using the ACM camera?ready template http://www.acm.org/sigs/pubs/proceed/template.html. Authors are required to submit their papers electronically in PDF format. The submission site URL will be available on our website shortly. All submissions should clearly present the author information including the names of the authors, the affiliations and the emails.

Workshop Co-chairs

Dr. Chidanand Apte, IBM Research
Prof. Nitesh V. Chawla, University of Notre Dame
Dr. Amol Ghoting, IBM Research
Prof. Yan Liu, University of Southern California
Dr. Jimeng Sun, IBM Research
Prof. Jie Tang, Tsinghua University, China
Dr. Ranga Raju Vatsavai, Oak Ridge National Laboratory


Steering Committee

Prof. Christos Faloutsos, Carnegie Mellon University
Prof. Robert Grossman, University of Illinois at Chicago
Prof. Jiawei Han, University of Illinois at Urbana-Champaign