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ClimKD 2011 : IEEE ICDM Workshop on Knowledge Discovery from Climate Data

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Link: http://www.nd.edu/~dial/climkd11
 
When Dec 10, 2011 - Dec 10, 2011
Where Vancouver, Canada
Submission Deadline Aug 5, 2011
Notification Due Sep 23, 2011
Final Version Due Oct 11, 2011
Categories    data mining   knowledge discovery   climate data   earth sciences
 

Call For Papers

WORKSHOP ON KNOWLEDGE DISCOVERY FROM CLIMATE DATA:
PREDICTION, EXTREMES, AND IMPACTS

In cooperation with IEEE ICDM 2011 | 10 December, 2011 (Vancouver, Canada)

Website: http://www.nd.edu/~dial/climkd11


DESCRIPTION
The analysis of climate data, both observed and model-generated, poses a number
of unique challenges: (i) massive quantities of data are available for mining,
(ii) the data is spatially and temporally correlated so the IID assumption does
not apply, (iii) the data-generating processes are known to be non-linear,
(iv) the data is potentially noisy, and (v) extreme events exist within the data.

In the computational data sciences, temporal, spatial and space-time data mining
differ fundamentally from traditional data mining in that the learning samples
are dependent, making auto- and cross-correlations important. Climate data mining
is based on geographic data and inherits the attributes of space-time data mining.
In addition, climate relationships are nonlinear, spatial correlations can be over
long range (teleconnections) and have long memory in time. Thus, in addition to
new or state of the art tools from temporal, spatial and spatio-temporal data
mining, new methods from nonlinear modeling and analysis are motivated along with
analysis of massive data for teleconnections and long-memory dependence.

Climate extremes may be inclusively defined as severe weather events as well as
significant regional changes in hydro-meteorology, which are caused or exacerbated
by climate change, and climate modelers and statisticians struggle to develop
precise projections of such phenomena. The ability to develop predictive insights
about extremes motivates the need to develop indices based on nonlinear
dimensionality reduction and anomaly analysis in space-time processes from massive
data. Knowledge discovery is broadly construed here to include high-performance
data mining of geographically-distributed climate model outputs and observations,
analysis of space-time correlations and teleconnections, geographical analyses of
extremes and their consequences obtained through fusion of heterogeneous climate
and GIS data along with their derivatives, geospatial-temporal uncertainty
quantification, as well as scalable geo-visualization for decision support.


TOPICS OF INTEREST include but are not limited to:
- Methods for mining climate datasets for patterns, trends, or extremes
- Complex networks and climate
- Spatio-temporal data mining
- Mining for rare events or phenomena in climate data
- Algorithms and implementations for the analysis of climate data, including
) Patterns / Clusters
) Extremes / Outliers
) Change Detection
- Methods addressing the role of uncertainty in space-time prediction
- High-performance data mining for the analysis of climate data
- Studies assessing the impacts of climate change and/or extremes
- Applications that demonstrate success stories of knowledge discovery
from climate data


IMPORTANT DATES
Paper Submission: August 5, 2011
Notification to Authors: September 23, 2011
Camera-Ready Papers: October 11, 2011
Workshop: December 10, 2011

Paper Submission: This is an open call for papers. Only original and high-quality
papers (regular length 8 pages, short papers 6 pages) conforming to the ICDM 2011
guidelines will be considered for this workshop. Papers must be submitted using
the ICDM workshop paper submission system.

Proceedings: Accepted papers will be included in an ICDM Workshop Proceedings volume,
to be published by IEEE Computer Society press, which will also be archived in the
IEEE Digital Library.


WORKSHOP ORGANIZERS
Nitesh V Chawla, University of Notre Dame, USA
Auroop R Ganguly, Oak Ridge National Lab, USA
Vipin Kumar, University of Minnesota, USA
Michael Steinbach, University of Minnesota, USA
Karsten Steinhaeuser, University of Minnesota, USA

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