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Welcome to MADMatch

 

MADMatch is a fast and scalable Many-to-many Approximate Diagram Matching approach based on an Error-Tolerant Graph matching (ETGM) formulation. Given two diagrams to be compared, MADMatch considers discrepancies as edit operations (e.g., modifying, renaming, or merging entities) to which costs are assigned. The matching task is thus modeled as an optimization problem, to which is applied a tabu search enhanced by original use of lexical and structural information.
Several case studies, involving different types of diagrams and matching tasks, provided evidence that MADMatch provides better results, with respect to scalability and accuracy, than dedicated state-of-the-art algorithms such as UMLDiff, AURA, or PLTSDiff.
 
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