Model Oriented System Design on Big-data

Abstract

There are risks that any design hypotheses could be supported with big-data, when engineers focus on a particular part of the data intentionally or accidentally, for the reason that big-data include huge and various kinds of data to mislead the reasoning of the hypotheses. The design process of diagnosis system for vacuum pumps in semiconductor factories is picked up as a target of case study. Errors of the hypotheses in the design are clarified by visualizing reasoning process of the design. The visualization of the reasoning process guides the engineer to elaborate the proper design models on the correct hypothesis through cycles of deductive and inductive reasoning with both data and their domain knowledge. The diagnosis system is re-designed and implemented on the established design models and the accuracy of diagnosis of the system is confirmed through the field test. We emphasize that the design method led by the design model on the deep domain knowledge is indispensable for designing system on big-data in the paper.