Monitorización y análisis de configuraciones y esquemas en motores de bases de datos relacionales

Por mi experiencia profesional, las bases de datos relacionales son uno de los componentes más sensibles dentro de una aplicación. Un mínimo cambio no deseado en estas puede desencadenar un resultado catastrófico que puede ir desde una denegación de servicio hasta la perdida de datos. Si bien, una base de datos bien configurada no debería ser modificable de forma no deseada, esto no se cumple en la mayoría de las empresas. En este proyecto se plantea la creación de un software cuya principal tarea será la de monitorizar instancias de bases de datos relacionales, así como su configuración y sus posibles mejoras. Este trabajo abordará la prueba de concepto de este software que será centrada principalmente en el proveedor de bases de datos de Microsoft, SQL Server. De forma conjunta, implementará su equivalente para la base de datos MySQL, siendo también compatible con ciertas versiones de MariaDB. De todas formas, la implementación de este proyecto estará abierta a la introducción de nuevos sistemas de monitorización para otros sabores de bases de datos

Automatic music composition by genetic programming

Automatic music composition is an area of research widely studied nowadays and many approaches have been proposed for this problem. This work is based on an existing project developed by the GRFIA which uses genetic programming for generating music melodies without human supervision. The project utilises a general-purpose library which is in charge of the genetic programming logic. The task of supervising the melodies is accomplished by a set of machine learning algorithms that are trained using a corpus of songs in order to select the best melodies generated. This final degree project develops a new library which replaces the one used by the original project. This new library implements some of the logic of genetic programming but the part in charge of selecting the best individuals has been developed using the multi-objective optimization algorithm NSGA-III. On the other hand, this project extends the binary tree structure used by the software. The current data model is able to store melodic and rhythm information and the proposed model is able to store harmonic information too. This change improves the way new melodies are generated. Finally, a comparative has been made using performance data and the overall score of the melodies generated. The result of the analysis is positive, but it has slightly improved in comparison to the original project. Even though, the two main goals, developing a new library and extending the model, have been successfully completed

Automatic music composition by genetic programming

Automatic music composition is an area of research widely studied nowadays and many approaches have been proposed for this problem. This work is based on an existing project developed by the GRFIA which uses genetic programming for generating music melodies without human supervision. The project utilises a general-purpose library which is in charge of the genetic programming logic. The task of supervising the melodies is accomplished by a set of machine learning algorithms that are trained using a corpus of songs in order to select the best melodies generated. This final degree project develops a new library which replaces the one used by the original project. This new library implements some of the logic of genetic programming but the part in charge of selecting the best individuals has been developed using the multi-objective optimization algorithm NSGA-III. On the other hand, this project extends the binary tree structure used by the software. The current data model is able to store melodic and rhythm information and the proposed model is able to store harmonic information too. This change improves the way new melodies are generated. Finally, a comparative has been made using performance data and the overall score of the melodies generated. The result of the analysis is positive, but it has slightly improved in comparison to the original project. Even though, the two main goals, developing a new library and extending the model, have been successfully completed