Data mining and ware housing

Abstract

Data mining is a combination of database and artificial intelligence technologies. Although the AI field has taken a major dive in the last decade; this new emerging field has shown that AI can add major contributions to existing fields in computer science. In fact, many experts believe that data mining is the third hottest field in the industry behind the Internet, and data warehousing. Data mining is really just the next step in the process of analyzing data. Instead of getting queries on standard or user-specified relationships, data mining goes a step farther by finding meaningful relationships in data. Relationships that were thought to have not existed or ones that give a more insightful view of the data. For example, a computer generated graph may not give the user any insight; however data mining can find trends in the same data that shows the user more precisely what is going on. Using trends that the end-user would have never thought to query the computer about. Without adding any more data, data mining gives a huge increase in the value added by the database. It allows both technical and non-technical users get better answers, allowing them to make a much more informed decision, saving their companies millions of dollars. Data Mining is a concept that is taking off in the commercial sector as a means of finding useful information out of gigabytes of data. While products for the commercial environment are starting to become available, tools for a scientific environment are much rarer (or even non-existent). Yet scientists have long had to search through reams of printouts and rooms full of tapes to find the gems that make up scientific discovery. This paper will explore some of the ad hoc methods generally used for Data Mining in the scientific community, including such things as scientific visualization, and outline how some of the more recently developed products used in the commercial environment can be adapted to scientific Data Mining.