Querying Temporal Drifts at Multiple Granularities

Abstract : 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 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.
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Pré-publication, Document de travail
Laboratory of Information Networking and Communication Sciences. 2015
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Contributeur : Vassilis Christophides <>
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Dernière modification le : jeudi 11 janvier 2018 - 06:27:31
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Sofia Kleisarchaki, Sihem Amer-Yahia, Ahlame Douzal-Chouakria, Vassilis Christophides. Querying Temporal Drifts at Multiple Granularities. Laboratory of Information Networking and Communication Sciences. 2015. 〈hal-01182742〉

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