CFP: The NASA Conference on Intelligent Data Understanding

CIDU-2010: The NASA Conference on Intelligent Data Understanding
Computer History Museum, Mountain View, CA, USA
October 5th - 7th, 2010

The NASA Conference on Intelligent Data Understanding is applications-oriented, with a focus on Earth Sciences, Space Sciences, and Aerospace and Engineering Systems Applications. The program committee of CIDU is soliciting theme-oriented papers that help propel one of the aforementioned applications, while also making contributions to machine learning and data mining. We invite papers that include a clear link between the domain (application area) and machine learning/data mining.

All submitted papers will be judged based on their technical/scientific merit, significance, originality, relevance, and clarity. Papers submitted to CIDU 2010 should be original work, not previously published in a peer-reviewed conference or journal, unless the authors submit a compelling position paper based on their prior published work, presenting the grand challenge problem for the synergy between the domain and data mining. While the conference is focused on the core application areas of Earth Sciences, Space Sciences, and Aerospace and Engineering Systems for the conference, we very much welcome papers that are more methodology oriented with applications that might cross-cut across the topics. The methodology papers must be motivated accordingly.

The proceedings of CIDU 2010 will be published by NASA and archived in the NASA Center for Aerospace Information and will be indexed by DBLP. Selected papers will be published in the journal Statistical Analysis and Data Mining.

Key dates:

  • Full paper manuscripts due: May 7, 2010
  • Full paper author notification: June 30, 2010
  • Full paper camera-ready due: July 20, 2010
  • Poster abstracts due: July 15, 2010
  • Poster decision notification: August 15, 2010
  • Early registration deadline: August 25, 2010

The core topics will include but not limited to:

  • Methodology Papers
    • Papers that may focus on methodological innovations focused on one or more application areas or hold the potential in one or more applications. The authors should clearly specify the potential for application of their work.
  • Earth Science Applications
    • Climate data sciences
    • GIS
    • Geospatial intelligence
    • Spatio-temporal data mining
    • Visual analytics for earth science data
    • High performance computing applications
    • Evaluation/validation techniques
    • Data mining success stories
  • Space Science Applications
    • On-board and real-time machine learning
    • Decision making under uncertainty
    • Constraint-driven data mining and machine learning
    • Event mining and robotic telescopes
    • Unsupervised and supervised learning in astrophysics
    • Highly scalable algorithms
    • Risk management in space missions
    • Classification in large sky surveys
    • Data mining success stories
  • Aerospace and Engineering Systems
    • Related government engineering applications (DOE, DOD, others)
    • Systems health applications
    • Anomaly detection, diagnostics, and prognostics from large data sets
    • Text mining in aerospace information systems
    • Data driven reliability modeling
    • Adaptive system monitoring
    • System model identification Large data set challenges
    • Exploratory mining of aerospace data
    • Privacy and security issues in aerospace data
    • Statistical process control using very large datasets
    • Data mining success stories

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