  Maximizing monotone submodular functions under cardinality constraints is a
classic optimization task with several applications in data mining and machine
learning. In this paper we study this problem in a dynamic environment with
consistency constraints: elements arrive in a streaming fashion and the goal is
maintaining a constant approximation to the optimal solution while having a
stable solution (i.e., the number of changes between two consecutive solutions
is bounded). We provide algorithms in this setting with different trade-offs
between consistency and approximation quality. We also complement our
theoretical results with an experimental analysis showing the effectiveness of
our algorithms in real-world instances.
