  Data is the king in the age of AI. However data integration is often a
laborious task that is hard to automate. Schema change is one significant
obstacle to the automation of the end-to-end data integration process. Although
there exist mechanisms such as query discovery and schema modification language
to handle the problem, these approaches can only work with the assumption that
the schema is maintained by a database. However, we observe diversified schema
changes in heterogeneous data and open data, most of which has no schema
defined. In this work, we propose to use deep learning to automatically deal
with schema changes through a super cell representation and automatic injection
of perturbations to the training data to make the model robust to schema
changes. Our experimental results demonstrate that our proposed approach is
effective for two real-world data integration scenarios: coronavirus data
integration, and machine log integration.
