Many existing complex space systems have a significant
amount of historical maintenance and problem data
bases that are stored in unstructured text forms. The problem that we address in this paper is the discovery of recurring anomalies and relationships between problem reports that may indicate larger systemic problems. We will illustrate our techniques on data from discrepancy reports regarding software anomalies in the Space Shuttle. These free text reports are written by a number of different people, thus the emphasis and wording vary considerably.

With Mehran Sahami from Stanford University, I'm putting together a book on text mining called "Text Mining: Theory and Applications" to be published by Taylor and Francis.