Integrated Data-Driven DSS in a Laboratory Environment

Decision support technologies have remained individualistic as primarily stand-alone platforms. The ability to access and integrate a wide range of such technologies in an Integrated Decision Technology Environment IDTE can potentially increase a users ability to create more complex decision support projects. A well-designed IDTE will allow users to identify, learn about, access, execute and integrate disparate decision technologies. Data-Driven DSS provide decision makers with the capability to store and sort vast amounts of data by leveraging data warehousing and data mining. These data-oriented decision technologies can assist decision makers in making better and more informed decisions in shorter durations of time. This thesis focuses on Data-Driven data mining decision technologies and how they can be integrated into an IDTE. In the process of identifying data mining technology requirements, the author first created a simple taxonomy characterized by the four categories of association, classification, clustering, and prediction. He then designed a database schema for storing the requisite data about data mining technologies, and case studies illustrating their use. Finally, he designed a simple, yet effective interface for navigating through the data-driven decision technology universe both at NPS and beyond. SQL commands for populating the various screens of the IDTE interface were provided to show proof of concept.