Data Integration using Machine Learning

Today, enterprise integration and cross-enterprise collaboration is becoming evermore important. The Internet of things, digitization and globalization are pushing continuous growth in the integration market. However, setting up integration systems today is still largely a manual endeavor. Most probably, future integration will need to leverage more automation in order to keep up with demand. This paper presents a first version of a system that uses tools from artificial intelligence and machine learning to ease the integration of information systems, aiming to automate parts of it. Three models are presented and evaluated for precision and recall using data from real, past, integration projects. The results show that it is possible to obtain F-0.5 scores in the order of 80% for models trained on a particular kind of data, and in the order of 60% - 70% for less specific models trained on a several kinds of data. Such models would be valuable enablers for integration brokers to keep up with demand, and obtain a competitive advantage. Future work includes fusing the results from the different models, and enabling continuous learning from an operational production system.