A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

Kolomvatsos, K., Papadopoulou, P., Anagnostopoulos, C. and Hadjiefthymiades, S. (2019) A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge. In: 18th IFIP Conference on e-Business, e-Services and e-Society (I3E 2019), Trondheim, Norway, 18-20 Sept 2019, pp. 138-150. ISBN 9783030293734 (doi:10.1007/978-3-030-29374-1_12)

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Abstract

Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos
Authors: Kolomvatsos, K., Papadopoulou, P., Anagnostopoulos, C., and Hadjiefthymiades, S.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-9743
ISBN:9783030293734
Published Online:14 August 2019
Copyright Holders:Copyright © 2019 FIP International Federation for Information Processing
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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