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IEEE ICDM Workshop on Knowledge Discovery from Climate Data

Call for Papers [ PDF | TXT ]

The Climate Change Challenge: Climate change and consequences are increasingly being recognized as among the most significant challenges facing humanity and our planet. The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4) shared the 2007 Nobel Peace Prize for providing evidence of human-induced warming at global and century scales. The clear and present need is to develop regional assessments of climate change and consequences, including but not limited to large regional hydro-meteorological changes and extreme events, extreme stresses on ecology, environment, key resources, critical infrastructures and society, as well as detection or attribution and a comprehensive characterization or reduction of uncertainty. A clear link needs to be developed between the science of climate change and the science of impacts analysis for facilitating the process.

Innovations in Data Mining: The analysis of climate data, both observed and modelgenerated, 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. 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 spatiotemporal 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 spacetime 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:

Paper Submissions

We invite regular paper submissions, work-in-progress, demo papers, and position papers. The papers must follow the IEEE ICDM format and be submitted through the ICDM Workshop Submission Site.

The regular papers can be up to 10 pages in length in the IEEE ICDM format. The position and work-in-progress papers should be a minimum of 2 pages in the IEEE ICDM format. We very much welcome demo papers that cater to GIS and visualization aspects of climate data sciences. We especially encourage inter-disciplinary papers. All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to workshop topics, originality, significance, and clarity.

Important Dates

Paper SubmissionAugust 8, 2009
Notification to AuthorsSeptember 8, 2009
Camera-Ready PapersSeptember 28, 2009

Instructions for Authors

In addition to the oral presentations, each author is required to prepare a poster describing their work. Easels and 36"x48" poster boards with push pins will be available. At minimum, we ask that you bring hardcopies of your slides; however, we encourage that you create a full poster. You may use this Poster Template or use a design of your own choosing.