Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such a...
Main Authors: | , , , , , , |
---|---|
Format: | info:eu-repo/semantics/article |
Language: | English |
Published: |
MDPI
2020
|
Subjects: | |
Online Access: | http://hdl.handle.net/10835/7529 |
_version_ | 1789406284674498560 |
---|---|
author | Rodríguez Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando Criado Rodríguez, Javier Ayala Palenzuela, Rosa María Alonso Montesinos, Joaquín Blas Capobianco Uriarte, María De Las Mercedes |
author_facet | Rodríguez Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando Criado Rodríguez, Javier Ayala Palenzuela, Rosa María Alonso Montesinos, Joaquín Blas Capobianco Uriarte, María De Las Mercedes |
author_sort | Rodríguez Gracia, Diego |
collection | DSpace |
description | In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings. |
format | info:eu-repo/semantics/article |
id | oai:repositorio.ual.es:10835-7529 |
institution | Universidad de Cuenca |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | dspace |
spelling | oai:repositorio.ual.es:10835-75292023-04-12T19:24:45Z Microservices and Machine Learning Algorithms for Adaptive Green Buildings Rodríguez Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando Criado Rodríguez, Javier Ayala Palenzuela, Rosa María Alonso Montesinos, Joaquín Blas Capobianco Uriarte, María De Las Mercedes adaptive systems machine learning microservices smart building In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings. 2020-01-17T09:29:00Z 2020-01-17T09:29:00Z 2019-08-09 info:eu-repo/semantics/article 2071-1050 http://hdl.handle.net/10835/7529 en https://www.mdpi.com/2071-1050/11/16/4320 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
spellingShingle | adaptive systems machine learning microservices smart building Rodríguez Gracia, Diego Piedra Fernández, José Antonio Iribarne Martínez, Luis Fernando Criado Rodríguez, Javier Ayala Palenzuela, Rosa María Alonso Montesinos, Joaquín Blas Capobianco Uriarte, María De Las Mercedes Microservices and Machine Learning Algorithms for Adaptive Green Buildings |
title | Microservices and Machine Learning Algorithms for Adaptive Green Buildings |
title_full | Microservices and Machine Learning Algorithms for Adaptive Green Buildings |
title_fullStr | Microservices and Machine Learning Algorithms for Adaptive Green Buildings |
title_full_unstemmed | Microservices and Machine Learning Algorithms for Adaptive Green Buildings |
title_short | Microservices and Machine Learning Algorithms for Adaptive Green Buildings |
title_sort | microservices and machine learning algorithms for adaptive green buildings |
topic | adaptive systems machine learning microservices smart building |
url | http://hdl.handle.net/10835/7529 |
work_keys_str_mv | AT rodriguezgraciadiego microservicesandmachinelearningalgorithmsforadaptivegreenbuildings AT piedrafernandezjoseantonio microservicesandmachinelearningalgorithmsforadaptivegreenbuildings AT iribarnemartinezluisfernando microservicesandmachinelearningalgorithmsforadaptivegreenbuildings AT criadorodriguezjavier microservicesandmachinelearningalgorithmsforadaptivegreenbuildings AT ayalapalenzuelarosamaria microservicesandmachinelearningalgorithmsforadaptivegreenbuildings AT alonsomontesinosjoaquinblas microservicesandmachinelearningalgorithmsforadaptivegreenbuildings AT capobiancouriartemariadelasmercedes microservicesandmachinelearningalgorithmsforadaptivegreenbuildings |