Edge-centric inferential modeling & analytics

Anagnostopoulos, C. (2020) Edge-centric inferential modeling & analytics. Journal of Network and Computer Applications, 164, 102696. (doi: 10.1016/j.jnca.2020.102696)

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Abstract

This work contributes to a real-time, edge-centric inferential modeling and analytics methodology introducing the fundamental mechanisms for (i) predictive models update and (ii) diverse models selection in distributed computing. Our objective in edge-centric analytics is the time-optimized model caching and selective forwarding at the network edge adopting optimal stopping theory, where communication overhead is significantly reduced as only inferred knowledge and sufficient statistics are delivered instead of raw data obtaining high quality of analytics. Novel model selection algorithms are introduced to fuse the inherent models' diversity over distributed edge nodes to support inferential analytics tasks to end-users/analysts, and applications in real-time. We provide statistical learning modeling and establish the corresponding mathematical analyses of our mechanisms along with comprehensive performance and comparative assessment using real data from different domains and showing its benefits in edge computing.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos
Authors: Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Journal of Network and Computer Applications
Publisher:Elsevier
ISSN:1084-8045
ISSN (Online):1084-8045
Published Online:13 May 2020
Copyright Holders:Copyright © 2020 The Author
First Published:First published in Journal of Network and Computer Applications 164:102696
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science