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K. D. Borne (Raytheon ITSS, GSFC), C. Y. Cheung (NASA, GSFC)
NASA's Astronomical Data Center (ADC) has established a researcher's guide to Data Mining Resources for Space Science (http://adc.gsfc.nasa.gov/adc/adc_datamining.html). Data mining is defined as ``an information extraction activity whose goal is to discover hidden facts contained in databases.'' Application of data mining tools and techniques will enable the data-intensive scientific discovery capabilities that will be required of the new envisioned National Virtual Observatory (NVO). The ADC's resource guide includes extensive reference material for the NVO and large astronomical surveys, as well as for related computer information technologies (e.g., XML and the GRID). Examples of several data mining techniques and related NVO science research scenarios will be presented. Of particular interest are data mining techniques of two types: (1) visual data mining, which includes graphical browse displays of statistical data; and (2) temporal data mining, with direct application to large time series databases, such as that generated by the MACHO project. Data mining challenges will also be presented, including: (a) distributed data mining across multiple data sources (i.e., the NVO); (b) data mining across multiple data types (e.g, fusion of search results from astronomical catalog and image databases); and (c) education and public outreach possibilities (e.g., search for incoming asteroids, or new comets, or new types of variable stars within the new huge astronomical survey databases). A possible ``NVO@Home'' implementation would naturally employ a combination of visual and temporal data mining techniques.
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The author(s) of this abstract have provided an email address for comments about the abstract: Kirk.Borne@gsfc.nasa.gov