Automated Interoperability based on Decision Tree for Schema Matching

Abstract

The idea of data integration is to provide standardized access to a simultaneous set of independent and possibly heterogeneous data sources within a given property. In these complexions, schema matching is referred to as the task of discovering semantic correspondences between elements of two determined data source schemas. These tasks are important to allow the integration of data and the interoperability of systems in different domains. This task is currently done manually, and prior research has uncovered the difficulty of automation. This article shows the importance of machine learning to create an automatic mapping facility for matching patterns with smarter models and integrating symbiotic methods to improve matching results. This is a new approach to reduce the time needed for schema matching tasks. Our contribution is based on a reference architecture and a prototype for smarter interoperability using a combination of machine learning based on smarter schema matching for mediation systems.