  There exists a large body of work on online drift detection with the goal of
dynamically finding and maintaining changes in data streams. In this paper, we
adopt a query-based approach to drift detection. Our approach relies on {\em a
drift index}, a structure that captures drift at different time granularities
and enables flexible {\em drift queries}. We formalize different drift queries
that represent real-world scenarios and develop query evaluation algorithms
that use different materializations of the drift index as well as strategies
for online index maintenance. We describe a thorough study of the performance
of our algorithms on real-world and synthetic datasets with varying change
rates.
