As organizations collect ever larger databases, they are beginning to realize the importance of mining the accumulated wisdom inherent in their data archives.
Data mining is a new field with exciting early successes.
These successes, however, are tempered by the difficulties involved in mining large, complex data sources across an organization. Furthermore, although the importance of background knowledge has been amply demonstrated, data mining methods are generally unable to make use of it on a large scale. We describe here a prototype system for preprocessing data in the context of background knowledge. Although running the mining tools and analyzing the results might be the most interesting parts of this process, selecting and preparing the data are often the most difficult and time-consuming. Furthermore, because of the iterative nature of data mining, the preprocessing phase may be repeated many times, making changes and incorporating the results of each mining operation into the next cycle.