Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto

Ilustraciones

Autores:: Gil Vera, Juan Carlos

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
dc.title.translated.eng.fl_str_mv	Recommendation System Model for guided tours, based on ubiquitous and context-sensitive computing
title	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
spellingShingle	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Computación ubicua Procesamiento electrónico de datos - Procesamiento distribuido Desarrollo de programas para computador Métodos orientados a objetos (Computadores) modelo de recomendación análisis de sentimientos visita guiada sensibilidad al contexto ubicuidad recommendation model sentiment analysis guided tour context sensitivity ubiquity
title_short	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
title_full	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
title_fullStr	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
title_full_unstemmed	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
title_sort	Modelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contexto
dc.creator.fl_str_mv	Gil Vera, Juan Carlos
dc.contributor.advisor.none.fl_str_mv	Ovalle Carranza, Demetrio Arturo
dc.contributor.author.none.fl_str_mv	Gil Vera, Juan Carlos
dc.contributor.orcid.spa.fl_str_mv	Gil Vera, Juan Carlos [0000-0002-2707-8276]
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
topic	000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Computación ubicua Procesamiento electrónico de datos - Procesamiento distribuido Desarrollo de programas para computador Métodos orientados a objetos (Computadores) modelo de recomendación análisis de sentimientos visita guiada sensibilidad al contexto ubicuidad recommendation model sentiment analysis guided tour context sensitivity ubiquity
dc.subject.lemb.none.fl_str_mv	Computación ubicua Procesamiento electrónico de datos - Procesamiento distribuido Desarrollo de programas para computador Métodos orientados a objetos (Computadores)
dc.subject.proposal.spa.fl_str_mv	modelo de recomendación análisis de sentimientos visita guiada sensibilidad al contexto ubicuidad
dc.subject.proposal.eng.fl_str_mv	recommendation model sentiment analysis guided tour context sensitivity ubiquity
description	Ilustraciones
publishDate	2023
dc.date.issued.none.fl_str_mv	2023
dc.date.accessioned.none.fl_str_mv	2024-08-26T14:18:29Z
dc.date.available.none.fl_str_mv	2024-08-26T14:18:29Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86753
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86753 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.indexed.spa.fl_str_mv	LaReferencia
dc.relation.references.spa.fl_str_mv	Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, 114324. https://doi.org/10.1016/j.eswa.2020.114324 Abowd, G. D., Ebling, M., Hunt, G., Hui, L., & Gellersen, H. W. (2002). Context-aware computing (Vol. 1, Número 3, p. 23). https://doi.org/10.1109/MPRV.2002.1037718 Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753 Aldayel, M., Al-Nafjan, A., Al-Nuwaiser, W. M., Alrehaili, G., & Alyahya, G. (2023). Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations. Electronics, 12(19), 4047. https://doi.org/10.3390/electronics12194047 Alkhafaji, A., Fallahkhair, S., & Haig, E. (2020). A theoretical framework for designing smart and ubiquitous learning environments for outdoor cultural heritage. Journal of Cultural Heritage, 46, 244–258. https://doi.org/10.1016/j.culher.2020.08.006 Ansari, S. A. (s/f). Building a recomendation engine with Scala (p. 2). Bickerton, E. (2017). Out of context. Apollo, 2017-July(July-August), 58–62. https://doi.org/10.5840/philtheol20186792 Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2–3), 87–129. https://doi.org/10.1007/BF00143964 Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005 Cena, F., Likavec, S., & Rapp, A. (2019). Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous Computing: State of the Art and Future Directions. Information Systems Frontiers, 21(5), 1085–1110. https://doi.org/10.1007/s10796-017-9818-3 Chalmers, M. (2004). A historical view of context. Computer Supported Cooperative Work: CSCW: An International Journal, 13(3–4), 223–247. https://doi.org/10.1007/s10606-004-2802-8 Chang, G., Healey, M. J., McHugh, J. A. M., & Wang, J. T. L. (2001). Web Mining. 43(8), 93–104. https://doi.org/10.1007/978-1-4615-1639-2_7 Chen, C.-C., & Tsai, J.-L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638. https://doi.org/10.1016/j.future.2017.02.028 Dey, Akd., A. (2001). Understanding and using context. Personal and Ubiquitous Computing. Retrieved from http://dl. acm. org/citation. cfm?id=593572. (2001). Understanding and using context. Personal and ubiquitous computing, 4–7. https://doi.org/10.1016/j.healthplace.2012.01.006 Dourish, P., & Damasceno, C. S. (2016). Ubiquitous computing. Dialogues on Mobile Communication, 804, 67–86. https://doi.org/10.4324/9781315534619 Esmaeili, L., Mardani, S., Golpayegani, S. A. H., & Madar, Z. Z. (2020). A novel tourism recommender system in the context of social commerce. Expert Systems with Applications, 149, 113301. https://doi.org/10.1016/j.eswa.2020.113301 Etaiwi, W., & Naymat, G. (2017). The Impact of applying Different Preprocessing Steps on Review Spam Detection. Procedia Computer Science, 113, 273–279. https://doi.org/10.1016/j.procs.2017.08.368 Feng, L., Apers, P. M. G., & Jonker, W. (2004). Towards context-aware data management for ambient intelligence. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3180, 422–431. https://doi.org/10.1007/978-3-540-30075-5_41 Ghafoori, H. R., Sadeghi-Niaraki, A., Alesheikh, A. A., & Choi, S.-M. (2022). Ubiquitous GIS based outdoor evacuation assistance: An effective response to earthquake disasters. International Journal of Disaster Risk Reduction, 81, 103232. https://doi.org/10.1016/j.ijdrr.2022.103232 Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022 Höpken, W., Fuchs, M., Zanker, M., & Beer, T. (2010). Context-Based Adaptation of Mobile Applications in Tourism. Information Technology & Tourism, 12(2), 175–195. https://doi.org/10.3727/109830510X12887971002783 Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., & Prasad, S. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54, 240–254. https://doi.org/10.1016/j.compenvurbsys.2015.09.001 Islam, M. F., & Fonzone, A. (2021). Bus passenger path choices after consulting ubiquitous real-time information. Travel Behaviour and Society, 23(August 2020), 226– 239. https://doi.org/10.1016/j.tbs.2021.01.001 Jeon, N. J., Leem, C. S., Kim, M. H., & Shin, H. G. (2007). A taxonomy of ubiquitous computing applications. Wireless Personal Communications, 43(4), 1229–1239. https://doi.org/10.1007/s11277-007-9297-9 Jiao, X., Xiao, Y., Zheng, W., Wang, H., & Hsu, C. H. (2019). A novel next new point-of- interest recommendation system based on simulated user travel decision-making process. Future Generation Computer Systems, 100, 982–993. https://doi.org/10.1016/j.future.2019.05.065 Kano, Y., & Nakajima, T. (2018). International Journal of Pervasive Computing and Communications Article information : International Journal of Pervasive Computing and Communications, 14(1), 15–32. Li, J., Yang, Y., Gong, X., Jiang, J., Lu, Y., Lu, J., & Xie, S. (2023). Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences. Electronics, 12(20), 4199. https://doi.org/10.3390/electronics12204199 Liang, Z. (2022). Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review. Electronics, 11(20), 3384. https://doi.org/10.3390/electronics1120338 Madeira, R. N. (2012). Personalization in pervasive spaces towards smart interactions design. 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2012, March, 548–549. https://doi.org/10.1109/PerComW.2012.6197568 Mcdonald, D. W. (s/f). Systems. 111–112. McKenzie, G., Janowicz, K., Gao, S., & Gong, L. (2015). How where is when? On the regional variability and resolution of geosocial temporal signatures for points of interest. Computers, Environment and Urban Systems, 54, 336–346. https://doi.org/10.1016/j.compenvurbsys.2015.10.002 Palomino, P. T., Toda, A. M., Rodrigues, L., Oliveira, W., Nacke, L., & Isotani, S. (2022). An ontology for modelling user’ profiles and activities in gamified education. Research and Practice in Technology Enhanced Learning, 18, 018. https://doi.org/10.58459/rptel.2023.18018 Park, H., Kwon, S., & Kwon, H.-C. (2009). Ontology-based Approach to Intelligent Ubiquitous Tourist Information System. Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications, 1–6. https://doi.org/10.1109/ICUT.2009.5405697 Potonniée, O. (2002). Ubiquitous Personalization: A Smart Card Based Approach. Proc. of 4th Gemplus Developer Conference. Raza, S., & Ding, C. (2019). Progress in context-aware recommender systems—An overview. Computer Science Review, 31, 84–97. https://doi.org/10.1016/j.cosrev.2019.01.001 Restrepo Medina, S. E. (2012). Modelo de Inteligencia Ambiental basado en la integración de Redes de Sensores Inalámbricas y Agentes Inteligentes. Bdigital.Unal.Edu.Co, 126–126. Salur, M. U., Aydin, I., & Alghrsi, S. A. (2019). SmartSenti: A Twitter-Based Sentiment Analysis System for the Smart Tourism in Turkey. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 1–5. https://doi.org/10.1109/IDAP.2019.8875922 Santos, V. (2013). Use of Social Paradigms in Mobile Context-aware Computing. Procedia Technology, 9, 100–113. https://doi.org/10.1016/j.protcy.2013.12.011 Schürholz, D., Kubler, S., & Zaslavsky, A. (2020). Artificial intelligence-enabled context- aware air quality prediction for smart cities. Journal of Cleaner Production, 271. https://doi.org/10.1016/j.jclepro.2020.121941 Villegas, N. M., Sánchez, C., Díaz-Cely, J., & Tamura, G. (2018). Characterizing context- aware recommender systems: A systematic literature review. Knowledge-Based Systems, 140, 173–200. https://doi.org/10.1016/j.knosys.2017.11.003 Zhang, B., Yin, C., David, B., Xiong, Z., & Niu, W. (2016). Facilitating professionals’ work- based learning with context-aware mobile system. Science of Computer Programming, 129, 3–19. https://doi.org/10.1016/j.scico.2016.01.008 Zheng, W., Liao, Z., & Lin, Z. (2020). Navigating through the complex transport system: A heuristic approach for city tourism recommendation. Tourism Management, 81, 104162. https://doi.org/10.1016/j.tourman.2020.104162
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dc.format.extent.spa.fl_str_mv	175 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Ingeniería de Sistemas
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ovalle Carranza, Demetrio Arturo19fbecc7d9324a7a37238ec3cfe6749bGil Vera, Juan Carlos218e80636038789db996e6f11657214dGil Vera, Juan Carlos [0000-0002-2707-8276]2024-08-26T14:18:29Z2024-08-26T14:18:29Z2023https://repositorio.unal.edu.co/handle/unal/86753Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesEl modelo propuesto pensado para funcionar en un contexto de visitas guiadas, se basa en el desarrollo de la ontología tourist en python usando la librería Owlready, y describe las entidades del modelo para visitas guiadas. La ontología permite aplicar los conceptos de ubicuidad y permite representar la sensibilidad al contexto en tres formas, con el contexto geográfico, temporal y ambiental. Para la visita guiada se considera el perfil del usuario, sus preferencias, el estado emocional y las evaluaciones de los lugares visitados, así mismo, el perfil, el itinerario y las características del sitio, las preferencias de transporte del usuario y las características de transporte del sitio. Se utilizó un lenguaje de ontologías que modela los conceptos y características del sistema de visitas guiadas que permite realizar inferencias con reglas usando el lenguaje SWRL con el razonador Pellet. Para el modelo de recomendación, se han desarrollado modelos de filtrado colaborativo, centrados en el usuario usando la media y la media ponderada de los puntajes de los sitios, y la información demográfica del usuario. Se han elaborado dos modelos de recomendación de filtrado colaborativo basado en clustering y usando filtrado con descomposición de valores singulares. Y un modelo de recomendación híbrido con una técnica de validación cruzada quíntuple. Todos los modelos fueron evaluados usando la métrica RMSE y para evaluar las predicciones se han usado las métricas de precisión, recall y F1 score. Finalmente, como aporte adicional a la tesis, se utilizó la técnica de análisis de sentimientos de Machine Learning para determinar el nivel de percepción del sitio de interés y así validar la utilidad del modelo para visitas guiadas. (Tomado de la fuente)The proposed model, designed to work in a guided tour context, is based on the development of the tourist ontology in python using the Owlready library, and describes the entities of the guided tour model. The ontology allows the application of the concepts of ubiquity and allows the representation of context sensitivity in three ways, with the geographic, temporal and environmental context. For the guided tour, the user profile, preferences, emotional state and evaluations of the places visited are considered, as well as the profile, itinerary and characteristics of the site, the user's transportation preferences and the transportation characteristics of the site. An ontology language was used that models the concepts and characteristics of the guided tour system that allows inferences to be made with rules using the SWRL language with the Pellet reasoner. For the recommendation model, collaborative filtering models have been developed, centered on the user using the mean and weighted mean of the scores of the sites, and the demographic information of the user. Two collaborative filtering recommendation models based on clustering and using filtering with singular value decomposition have been developed, as well as a hybrid recommendation model with a quintuple cross-validation technique. All models were evaluated using the RMSE metric and the precision, recall and F1 score metrics were used to evaluate the predictions. Finally, as an additional contribution to the thesis, the Machine Learning sentiment analysis technique was used to determine the level of perception of the site of interest and thus validate the usefulness of the model for guided tours.MaestríaMaestría en Ingeniería - Ingeniería de SistemasModelo que parte del diseño de una ontologíaInteligencia Artificial175 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - Sistemas000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónComputación ubicuaProcesamiento electrónico de datos - Procesamiento distribuidoDesarrollo de programas para computadorMétodos orientados a objetos (Computadores)modelo de recomendaciónanálisis de sentimientosvisita guiadasensibilidad al contextoubicuidadrecommendation modelsentiment analysisguided tourcontext sensitivityubiquityModelo de Sistema de Recomendación para visitas guiadas, basado en computación ubicua y sensible al contextoRecommendation System Model for guided tours, based on ubiquitous and context-sensitive computingTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferenciaAbbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, 114324. https://doi.org/10.1016/j.eswa.2020.114324Abowd, G. D., Ebling, M., Hunt, G., Hui, L., & Gellersen, H. W. (2002). Context-aware computing (Vol. 1, Número 3, p. 23). https://doi.org/10.1109/MPRV.2002.1037718Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753Aldayel, M., Al-Nafjan, A., Al-Nuwaiser, W. M., Alrehaili, G., & Alyahya, G. (2023). Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations. Electronics, 12(19), 4047. https://doi.org/10.3390/electronics12194047Alkhafaji, A., Fallahkhair, S., & Haig, E. (2020). A theoretical framework for designing smart and ubiquitous learning environments for outdoor cultural heritage. Journal of Cultural Heritage, 46, 244–258. https://doi.org/10.1016/j.culher.2020.08.006Ansari, S. A. (s/f). Building a recomendation engine with Scala (p. 2).Bickerton, E. (2017). Out of context. Apollo, 2017-July(July-August), 58–62. https://doi.org/10.5840/philtheol20186792Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2–3), 87–129. https://doi.org/10.1007/BF00143964Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005Cena, F., Likavec, S., & Rapp, A. (2019). Real World User Model: Evolution of User Modeling Triggered by Advances in Wearable and Ubiquitous Computing: State of the Art and Future Directions. Information Systems Frontiers, 21(5), 1085–1110. https://doi.org/10.1007/s10796-017-9818-3Chalmers, M. (2004). A historical view of context. Computer Supported Cooperative Work: CSCW: An International Journal, 13(3–4), 223–247. https://doi.org/10.1007/s10606-004-2802-8Chang, G., Healey, M. J., McHugh, J. A. M., & Wang, J. T. L. (2001). Web Mining. 43(8), 93–104. https://doi.org/10.1007/978-1-4615-1639-2_7Chen, C.-C., & Tsai, J.-L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638. https://doi.org/10.1016/j.future.2017.02.028Dey, Akd., A. (2001). Understanding and using context. Personal and Ubiquitous Computing. Retrieved from http://dl. acm. org/citation. cfm?id=593572. (2001). Understanding and using context. Personal and ubiquitous computing, 4–7. https://doi.org/10.1016/j.healthplace.2012.01.006Dourish, P., & Damasceno, C. S. (2016). Ubiquitous computing. Dialogues on Mobile Communication, 804, 67–86. https://doi.org/10.4324/9781315534619Esmaeili, L., Mardani, S., Golpayegani, S. A. H., & Madar, Z. Z. (2020). A novel tourism recommender system in the context of social commerce. Expert Systems with Applications, 149, 113301. https://doi.org/10.1016/j.eswa.2020.113301Etaiwi, W., & Naymat, G. (2017). The Impact of applying Different Preprocessing Steps on Review Spam Detection. Procedia Computer Science, 113, 273–279. https://doi.org/10.1016/j.procs.2017.08.368Feng, L., Apers, P. M. G., & Jonker, W. (2004). Towards context-aware data management for ambient intelligence. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3180, 422–431. https://doi.org/10.1007/978-3-540-30075-5_41Ghafoori, H. R., Sadeghi-Niaraki, A., Alesheikh, A. A., & Choi, S.-M. (2022). Ubiquitous GIS based outdoor evacuation assistance: An effective response to earthquake disasters. 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Tourism Management, 81, 104162. https://doi.org/10.1016/j.tourman.2020.104162EstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86753/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL71360447.2024.pdf71360447.2024.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemasapplication/pdf4020270https://repositorio.unal.edu.co/bitstream/unal/86753/2/71360447.2024.pdfad5d5acb10a7847fa4a606db83101c13MD52THUMBNAIL71360447.2024.pdf.jpg71360447.2024.pdf.jpgGenerated Thumbnailimage/jpeg5214https://repositorio.unal.edu.co/bitstream/unal/86753/3/71360447.2024.pdf.jpg55ab7d39d8a74d0fe6c6d4a46996bfc7MD53unal/86753oai:repositorio.unal.edu.co:unal/867532024-08-26 23:04:21.631Repositorio Institucional Universidad Nacional de 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