Data scientists are tasked with obtaining insights from data. However, suitable data is often
not immediately at hand, and there may be many potentially relevant datasets in a data lake …

Data marketplaces are expected to play a crucial role in tomorrow's data economy, but such
marketplaces are seldom commercially viable. Currently, there is no clear understanding of …

Inferring meta information about tables, such as column headers or relationships between
columns, is an active research topic in data management as we find many tables are …

Concern about the functional consequences of unprecedented loss in biodiversity has
prompted biodiversity–ecosystem functioning (BEF) research to become one of the most …

Question Answering (QA) systems aim at supplying precise answers to questions, posed by
users in a natural language form. They are used in a wide range of application areas, from …

Column type annotation is the task of annotating the columns of a relational table with the
semantic type of the values contained in each column. Column type annotation is an …

We apply foundation models to data discovery and exploration tasks. Foundation models
include large language models (LLMs) that show promising performance on a range of …

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 …

The large volumes of structured data currently available, from Web tables to open-data
portals and enterprise data, open up new opportunities for progress in answering many …

Abstract The Internet of Things (IoT) and continuous advances in data-gathering devices
and techniques have significantly increased the amount of relevant data that can be …