Reducing Complexity of Diagnostic Message Pattern Specification and Recognition on In-Bound Data Using Semantic Techniques

Different companies in the same line of business can have similar computer systems with built-in diagnostic routines, and the ability to regularly send error-driven or event-driven environmental diagnostic messages in XML back to the system manufacturer. The system manufacturer typically uses these to determine faults in the system. The outcome of this troubleshooting can also assist end-users and clients in solving problems, and provide the production team valuable information that can be used to improve future versions of the product. A Company merger could lead to the same team processing diagnostic messages from similar but different products, in different syntax, leading to complexity explosion of specifying and maintaining diagnostic message pattern specification and recognition for many different syntaxes. This research reduces the above complexity by extending ISO Schematron, the industry standard language for XML semantic constraints specification and validation, with conceptual rules. Pace University Knowledge Graphs are used to describe the concepts or classes relevant to the diagnostic messages of a system, and the new conceptual Schematron rules are introduced to specify diagnostic patterns on these concepts. Such conceptual diagnostic patterns are then converted automatically into concrete Schematron rules based on the syntax of the specific diagnostic messages. A complete prototype was designed and implemented to validate this new methodology.