Representative subschema extraction method for schemas in technological applications

Data handled in Statistical Process Control (SPC) are collected from several different sources e.g. inspection machines. Schema matching is a technique which enables to connect technology applications of the machines and the process control system to avoid data redundancy and to help application cooperation. Schema matching procedures use several components combined by means of weights. Earlier on, we have provided a methodology to optimize these schema matcher parameters to achieve faster processing speed enabling real-time applicability as well. Our optimization methodology, however, requires a learning schema set. There are several considerations pertaining to the ideal learning set, e.g. it should be minimal and representative. In this paper, we introduce a representative subschema elicitation method. Our results were validated on the PCB manufacturing process at our department.