A Neural Database for Answering Aggregate Queries on Incomplete Relational Data (Extended Abstract)

Real-world datasets are often incomplete due to data collection cost, privacy considerations or as a side effect of data integration/preparation. We focus on answering aggregate queries on such datasets, where data incompleteness causes the answers to be inaccurate. To address this problem, assuming typical relational data, existing work generates synthetic data to complete the database, a challenging task, especially in the presence of bias in observed data.