  Data integration has been recently challenged by the need to handle large
volumes of data, arriving at high velocity from a variety of sources, which
demonstrate varying levels of veracity. This challenging setting, often
referred to as big data, renders many of the existing techniques, especially
those that are human-intensive, obsolete. Big data also produces technological
advancements such as Internet of things, cloud computing, and deep learning,
and accordingly, provides a new, exciting, and challenging research agenda.
Given the availability of data and the improvement of machine learning
techniques, this blog discusses the respective roles of humans and machines in
achieving cognitive tasks in matching, aiming to determine whether traditional
roles of humans and machines are subject to change. Such investigation, we
believe, will pave a way to better utilize both human and machine resources in
new and innovative manners. We shall discuss two possible modes of change,
namely humans out and humans in. Humans out aim at exploring out-of-the-box
latent matching reasoning using machine learning algorithms when attempting to
overpower human matcher performance. Pursuing out-of-the-box thinking, machine
and deep learning can be involved in matching. Humans in explores how to better
involve humans in the matching loop by assigning human matchers with a
symmetric role to algorithmic matcher in the matching process.
