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Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17 - Schema Independent Relational Learning by Picado, Jose; Termehchy, Arash; Fern, Alan; Ataei, Parisa is a scholarly article available to read on EtoBox.

What is Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17 - Schema Independent Relational Learning about?

Learning novel relations from relational databases is an important problem with many applications. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same database may be represented under different schemas for various reasons, such as data quality, efficiency and usability. The output of current relational learning algorithms tends to vary quite substantially over the choice of schema. This variation complicates their off-the-shelf application. We introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We show that current algorithms are not schema independent. We propose Castor, a relational learning algorithm that achieves schema independence by leveraging data dependencies.

Author

Picado, Jose; Termehchy, Arash; Fern, Alan; Ataei, Parisa

Publisher

ACM Press

Published

2017

Language

EN