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 …

Data discovery refers to a set of tasks that enable users and downstream applications to
explore and gain insights from massive collections of data sources such as data lakes. In …

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 …

Web server access log files are text files containing important data about server activities,
client requests addressed to a server, server responses, etc. Large-scale analysis of these …

Efficient data discovery is crucial in the era of data-driven decisionmaking. However, current
practices face significant challenges due to the intricacies of identifying datasets with …

There is an abundance of data, but a large volume of it is unusable. Data may be noisy,
unstructured, stored in incompatible for direct analysis medium or format, and often …

The complex, large-scale semiconductor wafer manufacturing generates substantial diverse
data, creating management hurdles and making efficient use of historical scheduling data …

Abstract Knowledge engineering with respect to knowledge graphs and graph data in
general is becoming a more and more essential component of intelligent systems. Such …

Abstract The Data Lake Organization Problem consists of optimized data navigation
structures generation to reduce the user's time exploring all available data. The goal is to …

Machine learning (ML) models often require large amounts of data to perform well. When the
available data is limited, model trainers may need to acquire more data from external …