Data modeling for virtual observatory data mining

We propose a novel approach for index-tagging Virtual Observatory data files with descriptive statistics enabling rapid data mining and mathematical modeling. This is achieved by calculating at data collection time 6 standard moments as descriptive file tags. Data Change Detection Models are derived from these tags and used to filter databases for similar or dissimilar information such as stellar spectra, photometric data, images, and text. Currently, no consistent or reliable method for searching, collating, and comparing 2-D imagery exists. Traditionally, methods used to address these data problems are disparate and unrelated to text data mining and extraction. We explore the use of mathematical Data Models as a unifying tool set for enabling data mining across all data class domains.