  Data collection and labeling are critical bottlenecks in the deployment of
machine learning applications. With the increasing complexity and diversity of
applications, the need for efficient and scalable data collection and labeling
techniques has become paramount. This paper provides a review of the
state-of-the-art methods in data collection, data labeling, and the improvement
of existing data and models. By integrating perspectives from both the machine
learning and data management communities, we aim to provide a holistic view of
the current landscape and identify future research directions.
