LDMTA 2011 - The 3rd Workshop on Large-scale Data Mining: Theory and Applications (LDMTA 2011)

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


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