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Querying Temporal Drifts at Multiple Granularities

Sofia Kleisarchaki 1 Sihem Amer-Yahia 2 Ahlame Douzal-Chouakria 3 Vassilis Christophides 4, 5
1 HADAS - Heterogeneous and Adaptive distributed DAta management Systems
LIG - Laboratoire d'Informatique de Grenoble
2 SLIDE - ScaLable Information Discovery and Exploitation [Saint Martin d’Hères]
LIG - Laboratoire d'Informatique de Grenoble
3 LIG - Laboratoire d'Informatique de Grenoble
4 CSD-UOC - Computer Science Department [Crete]
5 MUSE - Measuring networks for enhancing USer Experience
Inria Paris-Rocquencourt
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.
Keywords : temporal drift detection Quering index trees Multiple granularities
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https://hal.archives-ouvertes.fr/hal-01208397
Contributor : Ahlame Douzal <>
Submitted on : Friday, October 2, 2015 - 3:18:43 PM
Last modification on : Monday, April 20, 2020 - 11:36:08 AM

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  • HAL Id : hal-01208397, version 1

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CNRS | INRIA | LIG | UGA | LIG-TDCGE-SLIDE

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Sofia Kleisarchaki, Sihem Amer-Yahia, Ahlame Douzal-Chouakria, Vassilis Christophides. Querying Temporal Drifts at Multiple Granularities. CIKM 2015, Oct 2015, Melbourne, Australia. ⟨hal-01208397⟩

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