  Modern audience measurement requires combining observations from disparate
panel datasets. Connecting and relating such panel datasets is a process termed
panel fusion. This paper formalizes the panel fusion problem and presents a
novel approach to solve it. We cast the panel fusion as a network flow problem,
allowing the application of a rich body of research. In the context of digital
audience measurement, where panel sizes can grow into the tens of millions, we
propose an efficient algorithm to partition the network into sub-problems.
While the algorithm solves a relaxed version of the original problem, we
provide conditions under which it guarantees optimality. We demonstrate our
approach by fusing two real-world panel datasets in a distributed computing
environment.
