Stable Tuple Embeddings for Dynamic Databases

TL;DR

This paper introduces stable tuple embedding methods for dynamic relational databases, focusing on maintaining high-quality embeddings amid database updates, with a novel schema-aware approach that outperforms existing techniques in dynamic scenarios.

Contribution

It proposes two embedding algorithms, including a schema-aware method called FoRWaRD, designed to be stable and efficient for evolving databases, advancing the state-of-the-art in dynamic data embedding.

Findings

FoRWaRD outperforms alternatives in dynamic settings.

Both methods achieve comparable static results to state-of-the-art.

FoRWaRD maintains high embedding quality with many new tuples.

Abstract

We study the problem of computing an embedding of the tuples of a relational database in a manner that is extensible to dynamic changes of the database. In this problem, the embedding should be stable in the sense that it should not change on the existing tuples due to the embedding of newly inserted tuples (as database applications might already rely on existing embeddings); at the same time, the embedding of all tuples, old and new, should retain high quality. This task is challenging since inter-dependencies among the embeddings of different entities are inherent in state-of-the-art embedding techniques for structured data. We study two approaches to solving the problem. The first is an adaptation of Node2Vec to dynamic databases. The second is the FoRWaRD algorithm (Foreign Key Random Walk Embeddings for Relational Databases) that draws from embedding techniques for general graphs and knowledge graphs, and is inherently utilizing the schema and its key and foreign-key constraints. We evaluate the embedding algorithms using a collection of downstream tasks of column prediction over geographical and biological domains. We find that in the traditional static setting, our two embedding methods achieve comparable results that are compatible with the state-of-the-art for the specific applications. In the dynamic setting, we find that the FoRWaRD algorithm generally outperforms and runs faster than the alternatives, and moreover, it features only a mild reduction of quality even when the database consists of more than half newly inserted tuples after the initial training of the embedding.