Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

The ubiquity of data lakes has created fascinating new challenges for data management
research. In this tutorial, we review the state-of-the-art in data management for data lakes …

Tabular data (or tables) are the most widely used data format in machine learning (ML).
However, ML models often assume the table structure keeps fixed in training and testing …

Existing techniques for unionable table search define unionability using metadata (tables
must have the same or similar schemas) or column-based metrics (for example, the values …

Dataset discovery from data lakes is essential in many real application scenarios. In this
paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes …

We present a new solution for finding joinable tables in massive data lakes: given a table
and one join column, find tables that can be joined with the given table on the largest …

Data analytics stands to benefit from the increasing availability of datasets that are held
without their conceptual relationships being explicitly known. When collected, these datasets …

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 …

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 …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …