<div class="csl-bib-body">
<div class="csl-entry">Aral, A., Erol-Kantarci, M., & Brandić, I. (2020). Staleness Control for Edge Data Analytics. <i>Proceedings of the ACM on Measurement and Analysis of Computing Systems</i>, <i>4</i>(2), 1–24. https://doi.org/10.1145/3392156</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/141671
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dc.description.abstract
A new generation of cyber-physical systems has emerged with a large number of devices that continuously generate and consume massive amounts of data in a distributed and mobile manner. Accurate and near real-time decisions based on such streaming data are in high demand in many areas of optimization for such systems. Edge data analytics bring processing power in the proximity of data sources, reduce the network delay for data transmission, allow large-scale distributed training, and consequently help meeting real-time requirements. Nevertheless, the multiplicity of data sources leads to multiple distributed machine learning models that may suffer from sub-optimal performance due to the inconsistency in their states. In this work, we tackle the insularity, concept drift, and connectivity issues in edge data analytics to minimize its accuracy handicap without losing its timeliness benefits. To this end, we propose an efficient model synchronization mechanism for distributed and stateful data analytics. Staleness Control for Edge Data Analytics (SCEDA) ensures the high adaptability of synchronization frequency in the face of an unpredictable environment by addressing the trade-off between the generality and timeliness of the model. Making use of online reinforcement learning, SCEDA has low computational overhead, automatically adapts to changes, and does not require additional data monitoring.
en
dc.language.iso
en
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dc.publisher
Association for Computing Machinery
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dc.relation.ispartof
Proceedings of the ACM on Measurement and Analysis of Computing Systems
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dc.subject
Computer Science (miscellaneous)
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dc.subject
Hardware and Architecture
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dc.subject
Computer Networks and Communications
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dc.subject
Safety, Risk, Reliability and Quality
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dc.title
Staleness Control for Edge Data Analytics
en
dc.type
Artikel
de
dc.type
Article
en
dc.description.startpage
1
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dc.description.endpage
24
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dc.type.category
Original Research Article
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tuw.container.volume
4
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tuw.container.issue
2
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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wb.publication.intCoWork
International Co-publication
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tuw.researchTopic.id
I4a
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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dcterms.isPartOf.title
Proceedings of the ACM on Measurement and Analysis of Computing Systems
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tuw.publication.orgunit
E194-04 - Forschungsbereich Data Science
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tuw.publisher.doi
10.1145/3392156
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dc.identifier.articleid
38
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dc.identifier.eissn
2476-1249
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dc.description.numberOfPages
24
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.facultyfocus
Information Systems Engineering (ISE)
de
wb.facultyfocus
Information Systems Engineering (ISE)
en
wb.facultyfocus.faculty
E180
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_2df8fbb1
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item.languageiso639-1
en
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item.openairetype
research article
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item.grantfulltext
none
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crisitem.author.dept
E194-04 - Forschungsbereich E-Commerce
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crisitem.author.dept
E194-04 - Forschungsbereich E-Commerce
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crisitem.author.orcid
0009-0007-0661-5937
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering