The EDAM project: mining mass spectra and more

The EDAM project is a collaborative effort between computer scientists and environmental chemists at Carleton College and UW-Madison. The goal is to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, is not adequate for studying particle dynamics and real-time correlations. This has led to the development of a new generation of real-time instruments that provide continuous or semi-continuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data, and dramatically increased the amounts of data to be collected, managed, and analyzed. We are investigating techniques for automatically labeling mass spectra from different kinds of aerosol mass spectrometers, and then analyzing and exploring the rich spatiotemporal information collected from multiple geographically distributed instruments. In this talk, I will present an overview of some novel data mining problems, describe some of the techniques we are developing to address them, and discuss the broader applicability of these techniques to problems from other domains.