Entity matching (EM) identifies data instances that refer to the same real-world entity, such
as (David Smith, UWMadison) and (DM Smith, UWM). This problem has been a long …

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 only generates value for a few organizations with expertise and resources to make
data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover …

A data ecosystem (DE) offers a keystone-player or alliance-driven infrastructure that enables
the interaction of different stakeholders and the resolution of interoperability issues among …

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 …

Medical ontologies are widely used to describe and organize medical terminologies and to
support many critical applications on healthcare databases. These ontologies are often …

Machine learning (ML) is playing an increasingly important role in data management tasks,
particularly in Data Integration and Preparation (DI&P). The success of ML-based …

Schema matching is a process that serves in integrating structured and semi-structured data.
Being a handy tool in multiple contemporary business and commerce applications, it has …

Schema matching aims to identify the correspondences among attributes of database
schemas. It is frequently considered as the most challenging and decisive stage existing in …

Data lakes (DLs) are large repositories of raw datasets from disparate sources. As more
datasets are ingested into a DL, there is an increasing need for efficient techniques to profile …