Machine-learning abstractions for component-based self-optimizing systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10474031" target="_blank" >RIV/00216208:11320/23:10474031 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uNHSdrayu8" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uNHSdrayu8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10009-023-00726-x" target="_blank" >10.1007/s10009-023-00726-x</a>
Alternative languages
Result language
angličtina
Original language name
Machine-learning abstractions for component-based self-optimizing systems
Original language description
This paper features an approach that combines machine-learning abstractions with a component model. We target modern self-optimizing systems and therefore integrate the machine-learning abstractions into our ensemble-based component model DEECo. We further endow the DEECo component model with abstractions for specifying self-optimization heuristics, which address coordination among multiple components. We demonstrate these abstractions in the context of an Industry 4.0 use case. We argue that incorporating machine learning and optimization heuristics is the key feature for modern smart systems, which learn over time and optimize their behavior at runtime to deal with uncertainty in their environment.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust – Enabling trust by fluid access control to data and physical resources in Industry 4.0 systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal on Software Tools for Technology Transfer
ISSN
1433-2779
e-ISSN
1433-2787
Volume of the periodical
25
Issue of the periodical within the volume
neuveden
Country of publishing house
DE - GERMANY
Number of pages
15
Pages from-to
717-731
UT code for WoS article
001093027600006
EID of the result in the Scopus database
2-s2.0-85175630104