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 …

We have made tremendous strides in providing tools for data scientists to discover new
tables useful for their analyses. But despite these advances, the proper integration of …

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 lakes are becoming increasingly prevalent for Big Data management and data
analytics. In contrast to traditional 'schema-on-write'approaches such as data warehouses …

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 …

Data integration has been extensively studied by the data management community and is a
core task in the data pre-processing step of ML pipelines. When the integrated data is used …

The launch of open governmental data portals (OGDPs) has popularized the open data
movement of last decade. Although the amount of data in OGDPs is increasing, their …

Addressing the increasing demand for data exchange has led to the development of data
markets that facilitate transactional interactions between data buyers and data sellers. Still …

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 …

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 …