A Data Quality Approach to Conformance Checks for Business Network Models

Abstract

The recent advances in programming languages, compilers and hardware allow software systems to process huge amounts of data. Therefore data is represented in domain specific models, capturing real-world artifacts as structured data for calculation and analysis. To guarantee data quality and reduce uncertainty within the domain information, model and content-based data conformance have to be checked. Domain model changes often require code changes for validations and redeployment to keep the pace. Current programming techniques and tools do not or only partially address this topic. In this paper, we present a novel approach to content-based validation of structured data for arbitrary domains. We define a validation programming model and show how a compiler and run-time system based on Deterministic Finite Automata(DFA) can be generated. We validated this approach by applying it to the domain of Network Mining (NM), which requires conformance checks for discovered raw data, used to compute business networks. The validation programs are illustrated by debugging the run-time system with textual and graphical tools.