Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning
This work addresses the laborious task of data integration for AI applications, focusing on unmanaged data where schema changes are common, representing an incremental improvement over existing methods that assume maintained schemas.
The paper tackles the problem of automating data integration in the face of schema changes, particularly for unmanaged data like heterogeneous and open data, by proposing a deep learning approach using super cell representation and data perturbations to enhance robustness, with experimental validation on real-world scenarios including coronavirus data and machine log integration.
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.