Assistance ontology of quality control for enterprise model using data mining

There are many quality domains in which ideas and concepts about quality are represented. The intelligent discovery assistants ontology of data mining (DM) processes was presented to compose and select the large space and non-trivial interaction in quality control for enterprise. We use a prototype to show that quality control for enterprise model is using the virtual enterprise quality ontology. A simple, but typical DM process was presented in the paper, which included preprocessing data, applying a data-mining algorithm, and post processing the mining results. It provides users with systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked and effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. Deeply research in the quality and ontology area is realized in protege with the format of OWL. Assistance ontology has the function to help mining workers selecting the algorithm, how to help selecting algorithm, the one prerequisite is that establishes good data mining method ontology. The intelligent discovery assistants search and deduct in the quality ontology. Finally, a study case is given to explain the practical application with the fault diagnosis bases on ontology, and was given encouraging results.