Abstract : There exists a large body of work on online drift detec- tion 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 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 mate- rializations 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.
https://hal.archives-ouvertes.fr/hal-01208397
Contributeur : Ahlame Douzal
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Soumis le : vendredi 2 octobre 2015 - 15:18:43
Dernière modification le : vendredi 25 mai 2018 - 12:02:07
