  Advances in deep learning have greatly widened the scope of automatic
computer vision algorithms and enable users to ask questions directly about the
content in images and video. This paper explores the necessary steps towards a
future Visual Data Management System (VDMS), where the predictions of such deep
learning models are stored, managed, queried, and indexed. We propose a query
and data model that disentangles the neural network models used, the query
workload, and the data source semantics from the query processing layer. Our
system, DeepLens, is based on dataflow query processing systems and this
research prototype presents initial experiments to elicit important open
research questions in visual analytics systems. One of our main conclusions is
that any future "declarative" VDMS will have to revisit query optimization and
automated physical design from a unified perspective of performance and
accuracy tradeoffs. Physical design and query optimization choices can not only
change performance by orders of magnitude, they can potentially affect the
accuracy of results.
