KDD for Science Data Analysis Issues and Examples

Contents Introduction Data Considerations Brief Case Studies Sky Survey Cataloging Finding Volcanoes on Venus Biosequence Databases Earth Geophysics Atmospheric Science Issues and Challenges Conclusion

Data Considerations Image Data Time-series and sequence data Numerical Vs Categorical values Structured and sparse data Reliability of Data

Brief Case Studies  Sky Survey Cataloging  Finding Volcanoes on Venus  Earth Geophysics  Atmospheric Science  Biosequence Databases

Sky Survey Cataloging The survey consists of 3 terabytes of image data containing an estimated 2 billion sky objects The basic problem is to generate a survey catalog which records the attributes of each object along with its class: star or galaxy To achieve this scientists developed the SKICAT system

Reasons why SKICAT was successful  The astronomers solved the feature extraction problem  Data mining methods contributed to solving difficult classification problems  Manual approaches were simply not feasible. Astronomers needed an automated classifier to make the most out of the data  Decision tree methods proved to be an effective tool for finding the important dimensions for this problem

Finding Volcanoes on Venus  Data collected by Magellan spacecraft  The first pass of Venus using the left looking radar resulted in 30, x 1000 pixel images  To help geologists analyze this data set, the JPL Adaptive Recognition Tool (JARtool) was developed

Motivation for using Data mining methods  Scientists did not know much about image processing or about the SAR properties. Hence they could easily label images but not design recognizers  There was little variation in illumination and orientation of objects of interest. Hence mapping from pixel space to feature space can be performed automatically  Geologists did not have any other easy means for finding the small volcanoes, hence they were motivated to cooperate by providing training data and other help

Earth Geophysics Two images taken before and after an earthquake and by repeatedly registering different local regions of the two images, it is possible to infer the direction and magnitude of ground motion due to the earthquake. Example of a geoscientific data mining system is Quakefinder which automatically detects and measures tectonic activity in the earths crust by examination of Satellite data

Atmospheric Science Data mining tool used is called CONQUEST Parallel testbeds were employed by Conquest to enable rapid extraction of spatio-temporal features for content based access. Some of the goals of the this tool is the development of “learning” algorithms which look for novel patterns, event clusters etc.

Retrieved Sea Level Pressure Fields

Biosequence Databases  The largest DNA database is GENBANK with a database of about 400 million letters of DNA from a variety of organisms  The pressing data mining tasks for biosequence are Find genes in the DNA sequences of various organisms. Some of the gene finding programs such as GRAIL, GeneID, GeneParser, Genie use neural nets and other AI or statistical methods

Issues and Challenges Feature Extraction Minority Classes High degree of Confidence Data mining task Relevant domain Knowledge Scalable machines and Algorithms

Conclusions KDD applications in science may in general be easier than applications in business, finance, or other areas. This is due to the fact that science end users typically know the data in intimate detail.