  Data scientists are constantly facing the problem of how to improve
prediction accuracy with insufficient tabular data. We propose a table
enrichment system that enriches a query table by adding external attributes
(columns) from data lakes and improves the accuracy of machine learning
predictive models. Our system has four stages, join row search, task-related
table selection, row and column alignment, and feature selection and
evaluation, to efficiently create an enriched table for a given query table and
a specified machine learning task. We demonstrate our system with a web UI to
show the use cases of table enrichment.
