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 …

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 collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

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 …

Correctly detecting the semantic type of data columns is crucial for data science tasks such
as automated data cleaning, schema matching, and data discovery. Existing data …

Deep learning based techniques have been recently used with promising results for data
integration problems. Some methods directly use pre-trained embeddings that were trained …

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 …

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 …

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

Detecting the semantic types of data columns in relational tables is important for various
data preparation and information retrieval tasks such as data cleaning, schema matching …