Abstract: 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 talk, we present 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.
Restore: Neural Data Completion for Tabular Data
Benjamin Hilprecht (Technische Universität Darmstadt)
About the presenter: Benjamin Hilprecht is very interested in bringing together machine learning and database systems. Having completed his Doctor of Science in Computer Science from Technische Universität Darmstadt, Hilprecht has worked extensively in data management, leading to his recognition as the runner-up for the Jim Gray Dissertation Award by Sigmod in 2023.