Knowledge discovery in databases (KDD) is an area that has become important to organizations that search for trends and useful information from their raw database information.
Developmental and Operational Processes for Agent-Oriented Database Navigation for Knowledge Discovery
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Knowledge discovery in databases (KDD) is an area that has become important to organizations that search for trends and useful information from their raw database information. KDD can be a tedious and repetitive human-driven process with respect to extracting the relevant datasets from databases for processing in the relevant learning algorithms. We investigate an approach where agents can control the extraction of the data-sets. We show a software developmental process and paradigm for programming information agents to extract data-sets based on a methodology we refer to as "extraction hints". We discuss what data modeling approaches can be used to allow these information agents to be reusable across various domains and databases. Lastly, using the aviation domain for motivation, we show the design of an agent architecture toward the further automation of KDD using agents.