Querying Temporal Drifts at Multiple Granularities (Technical Report)
Data Structures and Algorithms
2016-05-16 v2
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 {\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.
Keywords
Cite
@article{arxiv.1605.02772,
title = {Querying Temporal Drifts at Multiple Granularities (Technical Report)},
author = {Sofia Kleisarchaki and Sihem Amer-Yahia and Ahlame Douzal-Chouakria and Vassilis Christophides},
journal= {arXiv preprint arXiv:1605.02772},
year = {2016}
}