  Classical approaches for OLAP assume that the data of all tables is complete.
However, in case of incomplete tables with missing tuples, classical approaches
fail since the result of a SQL aggregate query might significantly differ from
the results computed on the full dataset. Today, the only way to deal with
missing data is to manually complete the dataset which causes not only high
efforts but also requires good statistical skills to determine when a dataset
is actually complete. In this paper, we propose an automated approach for
relational data completion called ReStore using a new class of (neural)
schema-structured completion models that are able to synthesize data which
resembles the missing tuples. As we show in our evaluation, this efficiently
helps to reduce the relative error of aggregate queries by up to 390% on
real-world data compared to using the incomplete data directly for query
answering.
