*** 9318773 Tuzhilin More and more application domains, from financial market analysis to weather prediction, from monitoring supermarket purchases to monitoring satellite images, are becoming increasingly data-intensive. For this reason, the area of knowledge discovery in databases has recently attracted much interest of database researchers. Since many data mining applications are temporal in nature, it is important to study the problems of pattern discovery in the temporal database context. The purpose of this project is to (1) develop a framework for the characterization and classification of temporal patterns; (2) identify and characterize various types of pattern discovery problems within the proposed framework; (3) evaluate the applicability of existing techniques from artificial intelligence, operations research, signal processing, statistical time-series analysis, and other related disciplines to the problems from part (2), and to develop new temporal pattern discovery techniques, whenever necessary -- also these techniques should be made more efficient, if possible; and (4) parallelize the discovery algorithms in order to make them computationally feasible; this is important because temporal pattern discovery problems typically deal with large volumes of data. As a result of this project, a system will be developed that helps a user to discover knowledge from a large volume of temporal data. This research will provide a better theoretical understanding of the problems of pattern discovery in temporal databases, as well as provide some practical tools for finding patterns in temporal data. Potential applications of this work include financial, marketing, and medical applications, among others. ***
