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Virtually every sizable organization nowadays is building a form of a datalake. In theory, every department or team in the organization would enrichtheir datasets with metadata, and store them in a central data lake. Thosedatasets can then be combined in different ways and produce added value to theorganization. In practice, though, the situation is vastly different: eachdepartment has its own privacy policies, data release procedures, and goals. Asa result, each department maintains its own data lake, leading to data silos.For such data silos to be of any use, they need to be integrated. This paperpresents SiMa, a method for federating data silos that consistently finds morecorrect relationships than the state-of-the-art matching methods, whileminimizing wrong predictions and requiring 20x to 1000x less time to execute.SiMa leverages Graph Neural Networks (GNNs) to learn from the existing columnrelationships and automated data profiles found in data silos. Our method makesuse of the trained GNN to perform link prediction and find new columnrelationships across data silos. Most importantly, SiMa can be trainedincrementally on the column relationships within each silo individually, anddoes not require consolidating all datasets into one place.

SiMa: Effective and Efficient Data Silo Federation Using Graph Neural Networks