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CIDU 2012 - 2012 Conference on Intelligent Data Understanding (CIDU 2012)

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Category Data Mining; Machine Learning; Earth & Environmental Systems; Space Science; Aerospace & Engineering Systems

Deadline: May 18, 2012 | Date: October 24, 2012-October 26, 2012

Venue/Country: Colorado, U.S.A

Updated: 2012-02-13 19:50:11 (GMT+9)

Call For Papers - CFP

CIDU 2012: Conference on Intelligent Data Understanding

Boulder, Colorado, USA

October 24-26, 2012

Conference Website:

Contact E-Mail:


The 2012 Conference on Intelligent Data Understanding (CIDU 2012) is organized under the theme of "Bringing Data and Models Together" and will attract top researchers and practitioners in the field of data mining focusing on applications to Earth & Environmental Systems, Space Science, and Aerospace & Engineering Systems. The Organizing Committee is soliciting theme-oriented papers that advance one of these areas through the use of data mining or machine learning techniques. We invite papers that include a clear link between the domain and analysis methods, and papers that give perspectives on methods to bring data-driven and model-based methods together are particularly sought. We also invite submission of 2-page extended abstracts for posters reporting new and interesting results, ideas, or work-in-progress.

All papers and posters will be peer-reviewed based on technical merit, significance, originality, relevance, and clarity. Papers should be no more than 8 pages in two-column format and describe original work not previously published in a refereed conference or journal. The CIDU proceedings will be published by the NASA Center for Aerospace Information. Selected papers will be invited to be extended for consideration in the journal Statistical Analysis & Data Mining.


Paper Submissions Due: May 18, 2012

Acceptance Notification: July 27, 2012

Camera-Ready Papers Due: Aug 10, 2012

Poster Abstracts Due: Aug 10, 2012

Poster Decision Notification: Aug 24, 2012


Methodology Papers

Algorithmic or methodological innovations that are relevant to one or more application areas or hold such potential. We also invite papers with a data visualization component.

Earth & Environmental Systems Applications

- Climate, weather and ecology data sciences

- Climate modeling

- Geographic information systems

- Geospatial intelligence

- Spatio-temporal data mining

- Forecasting and decision support

Space Science Applications

- On-board and real-time machine learning

- Decision making under uncertainty

- Constraint-driven data mining and learning

- Event mining and robotic telescopes

- Un/supervised learning in astrophysics

- Classification in large sky surveys

Aerospace and Engineering Systems

- Systems health applications

- Anomaly detection for large datasets

- Text mining in aerospace information systems

- Data driven reliability modeling

- Adaptive system monitoring

- Exploratory mining of aerospace data

- Privacy & security issues in aerospace data

- Statistical process control

- Real-time prediction and decision support


General Chairs:

Nitesh V. Chawla, University of Notre Dame

Ashok N. Srivastava, NASA Ames Research Center

Area Chairs:

Earth & Environmental Systems ? John Williams, National Center for Atmospheric Research

Space Science ? Michael J. Way, NASA Goddard Institute for Space Studies ? Space Science

Aerospace & Engineering Systems ? Maxime Gariel, Rockwell Collins Control Technologies

NESC Chair:

Robert Beil, NASA Engineering and Safety Center

Poster Chair:

Kanishka Bhaduri, MCT/NASA Ames Research Center

Proceedings Chair:

Kamalika Das, SGT/NASA Ames Research Center

Publicity & Communications Chair:

Karsten Steinhaeuser, University of Minnesota

Local Arrangements Chair:

Andrew Weekley, National Center for Atmospheric Research


CIDU gratefully acknowledges support from the National Center for Atmospheric Research, the American Meteorological Society, the University of Notre Dame, and NASA.

Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
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