The goal of Active Template research is to create a single, unified environment that a data analyst can use to carry out a knowledge discovery project, and to deliver the resulting solution in the form of an Active Template. An Active Template is a hyper-linked information structure that tightly integrates actions (executable programs and commands), results (models, datasets, predictions, reports), and documentation (explanations of decisions, actions, and results). The use of Active Templates provides a number of benefits, including user guidance, improved documentation of actions and results, and increased reuse of previous work.

We approach the problem of stock selection from the perspective ofknowledge discovery in databases: given a database of several years ofquarterly information on over a thousand companies, discover patternsin the data that will allow one to predict which stocks are likely to haveexceptional returns in the future. The database includes measures oftrends in the stocks" prices as well as fundamental data on the companies.For this task we employed the Recon system, which is able to...

High-quality financial databases have existed for many years, but human analysts can only scratch the surface of the wealth of knowledge buried in this data. Using the rule-induction technology in the Recon data-mining system, an investment strategy based purely on the learned rules can generate significant profits.

To aid in making investment decisions, financial analysts purchase large amounts of data from financial data providers. They use this historical data to develop financial models for making predictions and assessing risk about current dat about current dataa. Unfortunately, these database often contain errors and omissions of important information. Analysts are dependent upon the quality of these databases—missing data can prevent computation of key values and, more dangerously, incorrect data can cause their models to produce erroneous results without the analyst's knowledge. Because of the importance of accurate data, and the large volume of data involved, data providers and consumers have a need to develop advanced methods for data cleaning: the process of identifying and correcting incomplete and incorrect information in databases. This paper describes how the Recon data mining system has been used to clean financial databases. Recon incorporates several data mining modules into a single, uniform framework: data visualization, deductive databases, and rule induction. The data visualization component supports the visual detection of outliers and other unusual phenomena. The deductive database enables analysts to capture, codify, and apply corporate knowledge about data integrity. The rule induction module creates error detection rules by generalizing from known errors to detect suspicious data entries in the rest of the data. The collaborative use of these three modules yields superior error detection over the application of any single data mining technique.

Database mining is the process of finding previously unknown rules and relations in large databases. Often, several database mining techniques must be used cooperatively in a single application. In this paper we present the Recon database mining framework, which integrates three database mining techniques: rule induction, rule deduction, and data visualization.