Harth, N. and Anagnostopoulos, C. (2018) Edge-Centric Efficient Regression Analytics. In: 2018 IEEE International Conference on Edge Computing (EDGE), San Francisco, CA, USA, 02-07 Jul 2018, pp. 93-100. ISBN 9781538672389 (doi: 10.1109/EDGE.2018.00020)
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160937.pdf - Accepted Version 740kB |
Abstract
We introduce an edge-centric parametric predictive analytics methodology, which contributes to real-time regression model caching and selective forwarding in the network edge where communication overhead is significantly reduced as only model's parameters and sufficient statistics are disseminated instead of raw data obtaining high analytics quality. Moreover, sophisticated model selection algorithms are introduced to combine diverse local models for predictive modeling without transferring and processing data at edge gateways. We provide mathematical modeling, performance and comparative assessment over real data showing its benefits in edge computing environments.
| Item Type: | Conference Proceedings |
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| Status: | Published |
| Refereed: | Yes |
| 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 |
| ISBN: | 9781538672389 |
| Copyright Holders: | Copyright © 2018 IEEE |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
| Related URLs: |
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