http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/35 2026-10-05T06:56:59Z 2026-10-05T06:56:59Z Quevedo-Ortiz, Suriel Machorro-Cano, Isaac Rodríguez-Mazahua, Lisbeth Castro-Medina, Felipe Segura-Ozuna, Mónica Guadalupe López-Rodríguez, Ariel http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/815 2024-01-25T03:00:22Z 2022-12-26T00:00:00Z

Caracterización de los Tipos de Fragmentación en Sistemas Gestores de Bases de Datos Relacionales Quevedo-Ortiz, Suriel; Machorro-Cano, Isaac; Rodríguez-Mazahua, Lisbeth; Castro-Medina, Felipe; Segura-Ozuna, Mónica Guadalupe; López-Rodríguez, Ariel En la actualidad, existen diversos sistemas gestores de bases de datos, los cuales compiten por tener el sistema que ofrezca el mejor rendimiento en las operaciones de lectura y escritura de datos. Una forma en la que estos sistemas logran escalar una base de datos es mediante métodos de fragmentación o partición de datos. La idea básica de este concepto es dividir una tabla original en tablas más pequeñas llamadas fragmentos. Aunque actualmente los sistemas gestores de bases de datos no relacionales se empezaron a hacer populares, este trabajo se centra en los sistemas gestores de bases de datos relacionales y la caracterización de los métodos de fragmentación o partición de tablas que estos ofrecen, comparando los tiempos de respuesta en operaciones de lectura y escritura con las bases de datos estándar TPC-H y TPC-E. En la actualidad, existen diversos sistemas gestores de bases de datos, los cuales compiten por tener el sistema que ofrezca el mejor rendimiento en las operaciones de lectura y escritura de datos. Una forma en la que estos sistemas logran escalar una base de datos es mediante métodos de fragmentación o partición de datos. La idea básica de este concepto es dividir una tabla original en tablas más pequeñas llamadas fragmentos. Aunque actualmente los sistemas gestores de bases de datos no relacionales se empezaron a hacer populares, este trabajo se centra en los sistemas gestores de bases de datos relacionales y la caracterización de los métodos de fragmentación o partición de tablas que estos ofrecen, comparando los tiempos de respuesta en operaciones de lectura y escritura con las bases de datos estándar TPC-H y TPC-E.

2022-12-26T00:00:00Z Marin-Vega, Humberto Alor-Hernández, Giner Bustos Lopez, Maritza Lopez-Martinez, Ignacio Hernandez-Chaparro, Norma Leticia http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/800 2024-01-17T03:00:16Z 2023-11-27T00:00:00Z

Extended Reality (XR) Engines for Developing Gamified Apps and Serious Games: A Scoping Review Marin-Vega, Humberto; Alor-Hernández, Giner; Bustos Lopez, Maritza; Lopez-Martinez, Ignacio; Hernandez-Chaparro, Norma Leticia Extended Reality (XR) is an emerging technology that enables enhanced interaction between the real world and virtual environments. In this study, we conduct a scoping review of XR engines for developing gamified apps and serious games. Our study revolves around four aspects:(1) existing XR game engines, (2) their primary features, (3) supported serious game attributes, and (4) supported learning activities. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model to conduct the scoping review, which included 40 primary studies published between 2019 and 2023. Our findings help us understand how current XR engines support the development of XR-enriched serious games and gamified apps for specific learning activities. Additionally, based on our findings, we suggest a set of pre-established game attributes that could be commonly supported by all XR game engines across the different game categories proposed by Lameras. Hence, this scoping review can help developers (1) select important game attributes for their new games and (2) choose the game engine that provides the most support to these attributes. Extended Reality (XR) is an emerging technology that enables enhanced interaction between the real world and virtual environments. In this study, we conduct a scoping review of XR engines for developing gamified apps and serious games. Our study revolves around four aspects:(1) existing XR game engines, (2) their primary features, (3) supported serious game attributes, and (4) supported learning activities. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model to conduct the scoping review, which included 40 primary studies published between 2019 and 2023. Our findings help us understand how current XR engines support the development of XR-enriched serious games and gamified apps for specific learning activities. Additionally, based on our findings, we suggest a set of pre-established game attributes that could be commonly supported by all XR game engines across the different game categories proposed by Lameras. Hence, this scoping review can help developers (1) select important game attributes for their new games and (2) choose the game engine that provides the most support to these attributes.

2023-11-27T00:00:00Z Machorro-Cano, Isaac Ríos-Méndez, Ingrid Aylin Palet-Guzmán, Jose Antonio Rodriguez-Mazahua, Nidia Rodriguez-Mazahua, Lisbeth Alor-Hernández, Giner Olmedo-Aguirre, Jose Oscar http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/799 2024-01-17T03:00:15Z 2023-12-21T00:00:00Z

Medical Opinions Analysis about the Decrease of Autopsies Using Emerging Pattern Mining Machorro-Cano, Isaac; Ríos-Méndez, Ingrid Aylin; Palet-Guzmán, Jose Antonio; Rodriguez-Mazahua, Nidia; Rodriguez-Mazahua, Lisbeth; Alor-Hernández, Giner; Olmedo-Aguirre, Jose Oscar An autopsy is a widely recognized procedure to guarantee ongoing enhancements in medicine. It finds extensive application in legal, scientific, medical, and research domains. However, declining autopsy rates in hospitals constitute a worldwide concern. For example, the Regional Hospital of Rio Blanco in Veracruz, Mexico, has substantially reduced the number of autopsies at hospitals in recent years. Since there are no documented historical records of a decrease in the frequency of autopsy cases, it is crucial to establish a methodological framework to substantiate any actual trends in the data. Emerging pattern mining (EPM) allows for finding differences between classes or data sets because it builds a descriptive data model concerning some given remarkable property. Data set description has become a significant application area in various contexts in recent years. In this research study, various EPM (emerging pattern mining) algorithms were used to extract emergent patterns from a data set collected based on medical experts’ perspectives on reducing hospital autopsies. Notably, the top-performing EPM algorithms were iEPMiner, LCMine, SJEP-C, Top-k minimal SJEPs, and Tree-based JEP-C. Among these, iEPMiner and LCMine demonstrated faster performance and produced superior emergent patterns when considering metrics such as Confidence, Weighted Relative Accuracy Criteria (WRACC), False Positive Rate (FPR), and True Positive Rate (TPR). An autopsy is a widely recognized procedure to guarantee ongoing enhancements in medicine. It finds extensive application in legal, scientific, medical, and research domains. However, declining autopsy rates in hospitals constitute a worldwide concern. For example, the Regional Hospital of Rio Blanco in Veracruz, Mexico, has substantially reduced the number of autopsies at hospitals in recent years. Since there are no documented historical records of a decrease in the frequency of autopsy cases, it is crucial to establish a methodological framework to substantiate any actual trends in the data. Emerging pattern mining (EPM) allows for finding differences between classes or data sets because it builds a descriptive data model concerning some given remarkable property. Data set description has become a significant application area in various contexts in recent years. In this research study, various EPM (emerging pattern mining) algorithms were used to extract emergent patterns from a data set collected based on medical experts’ perspectives on reducing hospital autopsies. Notably, the top-performing EPM algorithms were iEPMiner, LCMine, SJEP-C, Top-k minimal SJEPs, and Tree-based JEP-C. Among these, iEPMiner and LCMine demonstrated faster performance and produced superior emergent patterns when considering metrics such as Confidence, Weighted Relative Accuracy Criteria (WRACC), False Positive Rate (FPR), and True Positive Rate (TPR).

2023-12-21T00:00:00Z Machorro-Cano, Isaac Olmedo-Aguirre, Jose Oscar Alor-Hernández, Giner Rodriguez-Mazahua, Lisbeth Sanchez Morales, Laura Nely Perez-Castro, Nancy http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/798 2024-01-16T03:00:17Z 2023-12-20T00:00:00Z

Cloud-Based Platforms for Health Monitoring: A Review Machorro-Cano, Isaac; Olmedo-Aguirre, Jose Oscar; Alor-Hernández, Giner; Rodriguez-Mazahua, Lisbeth; Sanchez Morales, Laura Nely; Perez-Castro, Nancy Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and synchronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit. Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and synchronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit.

2023-12-20T00:00:00Z