Learning data transformation rules through examples: preliminary results

A key problem in many data integration tasks is that data is often in the wrong format and needs to be converted into a different format. This can be a very time consuming and tedious task. In this paper we propose an approach that can learn data transformations automatically from examples. Our approach not only identifies the transformations that are consistent with all examples, but also recommends the transformations that most likely transform the rest of unseen data correctly. The experimental results show that in six transformation scenarios our approach produces good results.