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 analysis often uses data sets that were collected for different purposes. Indeed, new
insights are often obtained by combining data sets that were produced independently of …

The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world
supervised machine learning (ML) tasks. In this paper, we study a new problem, namely …

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 …

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery
has become an important operation in data lake management. Existing approaches target …

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 …

Dataset discovery can be performed using search (with a query or keywords) to find relevant
data. However, the result of this discovery can be overwhelming to explore. Existing …

Data is a central component of machine learning and causal inference tasks. The availability
of large amounts of data from sources such as open data repositories, data lakes and data …

Data science growing success relies on knowing where a relevant dataset exists,
understanding its impact on a specific task, finding ways to enrich a dataset, and leveraging …