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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 querybased approach to drift detection. Our approach relies on a drift index, a structure that captures drift at different time granularities and enables flexible drift queries. We formalize different drift queries that represent real-world scenarios and develop query evaluation algorithms that use different materializations of thedoi:10.1145/2806416.2806436 dblp:conf/cikm/KleisarchakiACC15 fatcat:5oovtg5cpnfwzghsbk36figmfi