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

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Category LDMTA 2011

Deadline: May 07, 2011 | Date: August 21, 2011-August 24, 2011

Venue/Country: San Diego, U.S.A

Updated: 2011-03-25 09:16:03 (GMT+9)

Call For Papers - CFP

(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