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...

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Main Authors: 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
Format: info:eu-repo/semantics/article
Language:English
Published: MDPI 2020
Subjects:
Online Access:http://hdl.handle.net/10835/7529
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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.
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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
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