Quality-aware Aggregation & Predictive Analytics at the Edge

Harth, N. and Anagnostopoulos, C. (2018) Quality-aware Aggregation & Predictive Analytics at the Edge. In: IEEE Big Data 2017, Boston, MA, USA, 11-14 Dec 2017, pp. 17-26. ISBN 9781538627150 (doi: 10.1109/BigData.2017.8257907)

149980.pdf - Accepted Version



We investigate the quality of aggregation and predictive analytics in edge computing environments. Edge analytics require pushing processing and inference to the edge of a network of sensing & actuator nodes, which enables huge amount of contextual data to be processed in real time that would be prohibitively complex and costly to transfer on centralized locations. We propose a quality-aware, time-optimized edge analytics model that supports communication efficient predictive modeling within the edge network. Our idea rests on the capability of edge nodes to intelligently decide when and which data to deliver and process in light of minimizing the communication overhead and maximizing the quality of analytics results. We provide mathematical modeling, performance and comparative assessment over real datasets showing its benefits in edge computing environments.

Item Type:Conference Proceedings
Additional Information:This research is funded by the EU H2020 GNFUV Project/ Action RAWFIE-OC2-EXP-SCI, under the EC Future Internet Research Experimentation (FIRE+) initiative.
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos and Harth, Miss Natascha
Authors: Harth, N., and Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2018 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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