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Martin Pagel

“COCOA: COrrelation COefficient-Aware Data Augmentation” Short Paper Accepted at EDBT 2021

The short paper “COCOA: COrrelation COefficient-Aware Data Augmentation,” authored by Mahdi Esmailoghli, Jorge-Arnulfo Quiané-Ruiz, and Ziawasch Abedjan will be presented at the 24th International Conference on Extending Database Technology (EDBT 2021), the week of March 23-26, 2021.

Calculating correlation coefficients is one of the most used measures in data science. Although linear correlations are fast and easy to calculate, they lack robustness and effectiveness in the existence of non-linear associations. Rank-based coefficients, such as Spearman’s are more suitable. However, rank-based measures first require to sort the values and obtain the ranks, making their calculation super-linear. One of the use-cases that is affected by this is data enrichment for Machine Learning (ML) through feature extraction from large databases. Finding the most promising features from millions of candidates to increase the ML accuracy requires billions of correlation calculations. In this paper, we introduce an index structure that ensures rank-based correlation calculation in a linear time. Our solution accelerates the correlation calculation up to 500x in the data enrichment setting.

To download a preprint of the ”COCOA: COrrelation COefficient-Aware Data Augmentation,” paper visit:

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