Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such
tables, there has been tremendous progress on a variety of tasks in the area of table …

Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …

Deep learning based techniques have been recently used with promising results for data
integration problems. Some methods directly use pre-trained embeddings that were trained …

Query performance prediction, the task of predicting the latency of a query, is one of the most
challenging problem in database management systems. Existing approaches rely on …

Can AI help automate human-easy but computer-hard data preparation tasks that burden
data scientists, practitioners, and crowd workers? We answer this question by presenting …

Data curation–the process of discovering, integrating, and cleaning data–is one of the
oldest, hardest, yet inevitable data management problems. Despite decades of efforts from …

In this paper, we present Leva, an end-to-end system that boosts the performance of
machine learning tasks over relational data. Leva builds a relational embedding by …

The integration of multiple data sources is a common problem in a large variety of
applications. Traditionally, handcrafted similarity measures are used to discover, merge, and …

Entity resolution is a widely studied problem with several proposals to match records across
relations. Matching textual content is a widespread task in many applications, such as …