The multistrategy knowledge discovery tool, INLEN, is applied to databases consisting of economic and demographic facts and statistics about the countries of the world. Preliminary experiments focus on discerning and comparing various patterns in the status and development of countries in different regions of the world. These experiments have provided some interesting and often unexpected results, but they are only a beginning in exploring such data. By discovering patterns and exceptions such as the ones presented, domain experts may have new insights into national development patterns, predict future developments in certain countries, or use these discoveries to influence national policies. Users who are not experts in the domain may also make interesting discoveries with INLEN. The results of these initial experiments are presented and future paths of research in this domain are proposed. 1

Time is a common factor in most of the large databases. The time factor in such data can be utilized during the knowledge discovery process to overcome some limitations, such as the size of the data, of many learning systems. The paper presents an experiment for discovering knowledge in large temporal databases. The method divides the learning space into subspaces each corresponds to one time interval. The process of discovery is performed on the data available in each subspace, for a given set of decision classes, using the learning system AQ15. The method determines a set of attributes relevant to the discovery process using the important score (IS) system. The experiment is done on international TRADE data; the data is concerned with the imports and exports between the US and the world. The preliminary results show that strong relationships in the original data can be discovered over different subsets of the data.