Architecture for the 15-Minute City
Main Author: | |
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Publication Date: | 2021 |
Other Authors: | , |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10400.21/13805 |
Summary: | Finding a new home in a large city has increasingly become more complex, as people are concerned with not only the estate itself but also with its surroundings. The 15-Minute City concept, thinks of cities as a way to ensure that their residents within a 15-minute radius, can fulfill six essential functions: home, work, commerce, health care, education, and entertainment; which varies according to a chosen travel mode. To help people find properties that would fit them according to this concept, we have built an application that aims to provide an intuitive user interface that guides the user through the important decision of buying a house. To present relevant information to the user, we gathered information from three relevant sources, each with its unique challenges. The first source is estate data, extracted from the websites of local real estate agencies; second, city data, with points of interest relevant to the six essential functions mentioned previously; and at last, the user data provided by the user itself through our profile system which lets us understand his needs. System-wise, we built a reliable application following the microservices architecture guidelines, which future proofs our solution by segregating each part of the project and allowing it to scale easily, development and feature wise. The application scrapes, ingests, transforms and stores data regularly. The complete infrastructure is containerized using Docker and orchestrated by Kubernetes. With this application, we provided a scalable solution that allows users to select the best real estate taking into consideration the surrounding environment, tailored to their day-to-day needs, something that, as far as we know, is a novelty on real estate agency websites. |
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Architecture for the 15-Minute City15-Minute CitiesCloud computingGeographical systemsDistributed systemsMicroservicesFinding a new home in a large city has increasingly become more complex, as people are concerned with not only the estate itself but also with its surroundings. The 15-Minute City concept, thinks of cities as a way to ensure that their residents within a 15-minute radius, can fulfill six essential functions: home, work, commerce, health care, education, and entertainment; which varies according to a chosen travel mode. To help people find properties that would fit them according to this concept, we have built an application that aims to provide an intuitive user interface that guides the user through the important decision of buying a house. To present relevant information to the user, we gathered information from three relevant sources, each with its unique challenges. The first source is estate data, extracted from the websites of local real estate agencies; second, city data, with points of interest relevant to the six essential functions mentioned previously; and at last, the user data provided by the user itself through our profile system which lets us understand his needs. System-wise, we built a reliable application following the microservices architecture guidelines, which future proofs our solution by segregating each part of the project and allowing it to scale easily, development and feature wise. The application scrapes, ingests, transforms and stores data regularly. The complete infrastructure is containerized using Docker and orchestrated by Kubernetes. With this application, we provided a scalable solution that allows users to select the best real estate taking into consideration the surrounding environment, tailored to their day-to-day needs, something that, as far as we know, is a novelty on real estate agency websites.RCIPLMelo, LeonardoCruz, NunoDatia, Nuno2021-09-27T11:22:40Z2021-09-152021-09-15T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.21/13805enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-12T07:58:35Zoai:repositorio.ipl.pt:10400.21/13805Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:52:40.747917Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Architecture for the 15-Minute City |
title |
Architecture for the 15-Minute City |
spellingShingle |
Architecture for the 15-Minute City Melo, Leonardo 15-Minute Cities Cloud computing Geographical systems Distributed systems Microservices |
title_short |
Architecture for the 15-Minute City |
title_full |
Architecture for the 15-Minute City |
title_fullStr |
Architecture for the 15-Minute City |
title_full_unstemmed |
Architecture for the 15-Minute City |
title_sort |
Architecture for the 15-Minute City |
author |
Melo, Leonardo |
author_facet |
Melo, Leonardo Cruz, Nuno Datia, Nuno |
author_role |
author |
author2 |
Cruz, Nuno Datia, Nuno |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Melo, Leonardo Cruz, Nuno Datia, Nuno |
dc.subject.por.fl_str_mv |
15-Minute Cities Cloud computing Geographical systems Distributed systems Microservices |
topic |
15-Minute Cities Cloud computing Geographical systems Distributed systems Microservices |
description |
Finding a new home in a large city has increasingly become more complex, as people are concerned with not only the estate itself but also with its surroundings. The 15-Minute City concept, thinks of cities as a way to ensure that their residents within a 15-minute radius, can fulfill six essential functions: home, work, commerce, health care, education, and entertainment; which varies according to a chosen travel mode. To help people find properties that would fit them according to this concept, we have built an application that aims to provide an intuitive user interface that guides the user through the important decision of buying a house. To present relevant information to the user, we gathered information from three relevant sources, each with its unique challenges. The first source is estate data, extracted from the websites of local real estate agencies; second, city data, with points of interest relevant to the six essential functions mentioned previously; and at last, the user data provided by the user itself through our profile system which lets us understand his needs. System-wise, we built a reliable application following the microservices architecture guidelines, which future proofs our solution by segregating each part of the project and allowing it to scale easily, development and feature wise. The application scrapes, ingests, transforms and stores data regularly. The complete infrastructure is containerized using Docker and orchestrated by Kubernetes. With this application, we provided a scalable solution that allows users to select the best real estate taking into consideration the surrounding environment, tailored to their day-to-day needs, something that, as far as we know, is a novelty on real estate agency websites. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-27T11:22:40Z 2021-09-15 2021-09-15T00:00:00Z |
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conference object |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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publishedVersion |
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http://hdl.handle.net/10400.21/13805 |
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http://hdl.handle.net/10400.21/13805 |
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eng |
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eng |
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