Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality
tabular data for model training remains a significant obstacle. Numerous works have …

We explore the application of foundation models to data discovery and exploration tasks.
Foundation models are large language models (LLMs) that show promising performance on …

Enterprises have a growing need to identify relevant tables in data lakes; eg tables that are
unionable, joinable, or subsets of each other. Tabular neural models can be helpful for such …

Machine-learning from a disparate set of tables, a data lake, requires assembling features
by merging and aggregating tables. Data discovery can extend autoML to data tables by …

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare
has led to the emergence of data markets. A data market is any mechanism whereby the …

Unstructured data formats account for over 80% of the data currently stored, and extracting
value from such formats remains a considerable challenge. In particular, current approaches …

Discovering tables from poorly maintained data lakes is a significant challenge in data
management. Two key tasks are identifying joinable and unionable tables, crucial for data …

Exploratory data science is driving new platforms that assist data scientists with everyday
tasks, such as integration and wrangling, to assemble training datasets. Such tools take …

While graph machine learning, and notably graph neural networks (GNNs), have gained
immense traction in recent years, application is predicated on access to a known input graph …

Join discovery is a crucial part of exploration on data lakes. It often involves finding joinable
tables that are semantically relevant. However, data lakes often contain numeric tables with …