Distributed data mining is contemplated in the field of research and involves the application of the process of extracting knowledge about large volumes of information stored in distributed databases. Modern organizations require tools that perform tasks of prediction, forecasting, classification and others, online, on their databases that are located in different nodes interconnected through the Internet, in a way that allows them to improve the quality of their services. Clustering is one of the main modeling techniques of data mining which consists of dividing the information into different groups, internally the members of each group are very similar to each other and dissimilar to the members of the other groups. The resulting clusters or clusters allow us to predict patterns of behavior that can contribute to organizational decision-making. It is in this context that the present wo...

Machine learning is a branch of artificial intelligence that uses scientific computing, mathematics and statistics through automated techniques to solve problems based on classification, regression and clustering. Social demand refers to the need for service and product of the professional training process, expressed by interest groups, aimed at contributing to national development, as established by the quality assurance policy of university higher education and national licensing and accreditation models. In this context, this paper conducts research based on job positions of IT professionals posted n web portals, designs a machine learning process with an unsupervised approach, extracts occupational profiles, designs a multidimensional model, applies k-means clustering when determining clusters of job positions by similarity, and reports the results obtained.

La minería de datos distribuida está contemplada en el campo de la investigación e implica la aplicación del proceso de extracción de conocimiento sobre grandes volúmenes de información almacenados en bases de datos distribuidas. Las organizaciones modernas requieren de herramientas que realicen tareas de predicción, pronósticos, clasificación entre otros y en línea, sobre sus bases de datos que se ubican en diferentes nodos interconectados a través de internet, de manera que les permita mejorar la calidad de sus servicios. El Clustering es una de las principales tecnicas de modelado de la mineria de datos la cual consiste en dividir la información en grupos diferentes, internamente los miembros de cada grupo son muy similares unos de otros y disimiles respecto a los miembos de los otros grupos. Los grupos o clusters resultantes permiten predecir patrones de comportamiento q...

A cloud-native application is a software specifically designed to run in the cloud, focusing on distributed, elastic, horizontally scaled, and microservice-based architecture with autonomous deployment. These applications are designed with cloud-native web architectures, operate on an elastic self-service platform, and stand out because of their resilience and elasticity. Continuous software engineering integrates requirements engineering, development, and operations in a continuous loop with reciprocal feedback to produce quality software. The present work proposes to design and implement a cloud-native application applied to the SIGCON case study from a continuous software engineering perspective. It uses the CaaS cloud service model, applies the BFF pattern in software construction, containerizes the frontend, backend, and storage, and presents the results.

A cloud-native application is a software specifically designed to run in the cloud, focusing on distributed, elastic, horizontally scaled, and microservice-based architecture with autonomous deployment. These applications are designed with cloud-native web architectures, operate on an elastic self-service platform, and stand out because of their resilience and elasticity. Continuous software engineering integrates requirements engineering, development, and operations in a continuous loop with reciprocal feedback to produce quality software. The present work proposes to design and implement a cloud-native application applied to the SIGCON case study from a continuous software engineering perspective. It uses the CaaS cloud service model, applies the BFF pattern in software construction, containerizes the frontend, backend, and storage, and presents the results.

Microservices are conceived as an architectural style focused on developing applications through a set of services, independent, scalable, collaborative, evolutionary, capable of adapting to complex ecosystems. On the other hand, DevOps is a paradigm that uses a set of principles focused on the continuous delivery and integration of software, this implies a new culture to develop and deploy software in highly collaborative and agile contexts aimed at reducing the gap between development and operations. It is in this context that the present work proposes an Architecture based on Microservices and DevOps for continuous software engineering and applies the proposal through a case study with the participation of development teams formed by the students of the Workshop courses of Software and Systems Construction of the academic semesters: 2018-1, 2018-2, 2019-1, 2019-2 and led by the author...

Microservices are conceived as an architectural style focused on developing applications through a set of services, independent, scalable, collaborative, evolutionary, capable of adapting to complex ecosystems. On the other hand, DevOps is a paradigm that uses a set of principles focused on the continuous delivery and integration of software, this implies a new culture to develop and deploy software in highly collaborative and agile contexts aimed at reducing the gap between development and operations. It is in this context that the present work proposes an Architecture based on Microservices and DevOps for continuous software engineering and applies the proposal through a case study with the participation of development teams formed by the students of the Workshop courses of Software and Systems Construction of the academic semesters: 2018-1, 2018-2, 2019-1, 2019-2 and led by the author...

Machine Learning no supervisado es una rama de la inteligencia artificial que utiliza técnicas automatizadas para resolver problemas basados en el descubrimiento de patrones o conglomerados de objetos según su posición geométrica en el espacio vectorial n dimensional, la calidad del agrupamiento depende de la complejidad, dimensionalidad y granularidad del dataset, de las estadísticas y de la distribución de los datos; Clustering es una técnica que recae en este rubro. Por otro lado, Las cualificaciones y perfiles ocupacionales estandarizados y actualizados es uno de los objetivos de las naciones, enfocados en mejorar la calidad y pertinencia de la educación y la formación para el trabajo; globalmente se cuenta con las cualificaciones ocupacionales ISCO-08 de la OIT y a nivel nacional con el CNPO y MNCP. En ese contexto, el presente trabajo realiza una investigación a partir de...

La minería de datos distribuida está contemplada en el campo de la investigación que implica la aplicación del proceso de extracción de conocimiento sobre grandes volúmenes de información almacenados en bases de datos distribuidas. Las organizaciones modernas requieren de herramientas que realicen tareas de predicción, pronósticos, clasificación entre otros y en línea, sobre sus bases de datos que se ubican en diferentes nodos interconectados a través de internet, de manera que les permita mejorar la calidad de sus servicios. En ese contexto, el presente trabajo realiza una revisión bibliográfica de las técnicas clustering k-means, elabora una propuesta concreta, desarrolla un prototipo de aplicación y concluye fundamentando los beneficios que obtendrían las organizaciones con su implementación.