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arXiv · 2305.19629

Measuring and Predicting the Quality of a Join for Data Discovery

Sergi Nadal·Raquel Panadero·Javier Flores·Oscar Romero
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Abstract

We study the problem of discovering joinable datasets at scale. We approach the problem from a learning perspective relying on profiles. These are succinct representations that capture the underlying characteristics of the schemata and data values of datasets, which can be efficiently extracted in a distributed and parallel fashion. Profiles are then compared, to predict the quality of a join operation among a pair of attributes from different datasets. In contrast to the state-of-the-art, we define a novel notion of join quality that relies on a metric considering both the containment and cardinality proportion between join candidate attributes. We implement our approach in a system called NextiaJD, and present experiments to show the predictive performance and computational efficiency of our method. Our experiments show that NextiaJD obtains greater predictive performance to that of hash-based methods while we are able to scale-up to larger volumes of data.

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Sergi Nadal, Raquel Panadero, Javier Flores, Oscar Romero. 2023-05-31. Measuring and Predicting the Quality of a Join for Data Discovery. https://arxiv.org/abs/2305.19629

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