Can Deep Neural Networks Predict Data Correlations from Column Names?

Abstract: Recent publications suggest using natural language analysis on database schema elements to guide tuning and profiling efforts. The underlying hypothesis is that state-of-the-art language processing methods, so-called LLMs, are able to extract information on data properties from schema text. This paper examines that hypothesis in the context of data correlation analysis: is it possible to find column pairs with correlated data by analyzing their names via LLMs? First, the paper introduces a novel benchmark for data correlation analysis, created by analyzing thousands of Kaggle data sets (and available for download). Second, it uses that data to study the ability of LLMs to predict correlation, based on column names. The analysis covers different LLMs, various correlation metrics, and a multitude of accuracy metrics. It pinpoints factors that contribute to successful predictions, such as the length of column names as well as the ratio of words. Finally, \rev{the study analyzes the impact of column types on prediction performance.} The results show that schema text can be a useful source of information and inform future research efforts, targeted at NLP-enhanced database tuning and data profiling.