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Recent publications suggest using natural language analysis on databaseschema elements to guide tuning and profiling efforts. The underlyinghypothesis is that state-of-the-art language processing methods, so-calledlanguage models, are able to extract information on data properties from schematext.  This paper examines that hypothesis in the context of data correlationanalysis: is it possible to find column pairs with correlated data by analyzingtheir names via language models? First, the paper introduces a novel benchmarkfor data correlation analysis, created by analyzing thousands of Kaggle datasets (and available for download). Second, it uses that data to study theability of language models to predict correlation, based on column names. Theanalysis covers different language models, various correlation metrics, and amultitude of accuracy metrics. It pinpoints factors that contribute tosuccessful predictions, such as the length of column names as well as the ratioof words. Finally, \rev{the study analyzes the impact of column types onprediction performance.} The results show that schema text can be a usefulsource of information and inform future research efforts, targeted atNLP-enhanced database tuning and data profiling.

Can Deep Neural Networks Predict Data Correlations from Column Names?