  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 critical issue. First, as machine learning is
becoming more widely-used, we are seeing new applications that do not
necessarily have enough labeled data. Second, unlike traditional machine
learning, deep learning techniques automatically generate features, which saves
feature engineering costs, but in return may require larger amounts of labeled
data. Interestingly, recent research in data collection comes not only from the
machine learning, natural language, and computer vision communities, but also
from the data management community due to the importance of handling large
amounts of data. In this survey, we perform a comprehensive study of data
collection from a data management point of view. Data collection largely
consists of data acquisition, data labeling, and improvement of existing data
or models. We provide a research landscape of these operations, provide
guidelines on which technique to use when, and identify interesting research
challenges. The integration of machine learning and data management for data
collection is part of a larger trend of Big data and Artificial Intelligence
(AI) integration and opens many opportunities for new research.
