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[Resumen]: Este trabajo tiene como objetivo principal conseguir diseñar y desarrollar una aplicación web que pueda servir a los entrenadores de baloncesto base a contar con una herramienta en la que analizar partidos sea más visual y útil, además de intentar que sea sencilla y recoja las características principales de un partido de baloncesto. Esta aplicación no solo permite recopilar las estadísticas relacionadas con un partido, sino que también permite recopilar las estadísticas de cada jugador por separado en un partido, mostrar los líderes en cada una de las estadísticas en cada partido, visualizar la posición de los los lanzamientos ocurridos durante el partido, incluso filtrándolos por jugador, el mapa de calor de esos propios lanzamientos, o realizar una comparativa con jugadores profesionales. Para esto se emplean no solo datos alfanuméricos, sino también georreferenciados, como son las posiciones en el campo donde se producen determinados eventos. Sumado esto a recopilar datos de la página de la liga ACB (Asociación de Clubes de Baloncesto) con el objetivo de realizar una comparativa con jugadores profesionales. La aplicación web consta de dos partes: un servidor implementado en Flask, contando con una base de datos relacional (PostgreSQL), y un cliente web implementado haciendo uso de Leaflet y de HTML. En este trabajo se ha seguido una metodología ágil basada en Scrum para la gestión de este proyecto, con iteraciones de corta duración y revisión y entrega incremental. Cada iteración, conocida como sprint, consta de una duración fija la mayoría de las veces, y tiene varias etapas: planificación, desarrollo, revisión y retrospectiva. Durante el sprint, el objetivo principal es el de implementar una selección de las funcionalidades prioritarias inicialmente marcadas, asegurando que cada incremento realizado funcione correctamente. Se realizan reuniones cada dos semanas con los tutores para recibir feedback y correcciones sobre el trabajo., [Abstract]: The main objective of this work is to design and develop a web application that can serve youth basketball coaches by providing a tool to analyze games in a more visual and useful way, while ensuring it is simple and includes the main characteristics of a basketball game.. This application not only allows for the collection of statistics related to a game, but also enables the collection of individual player statistics in a game, displays the leaders in each statistic for each game, visualizes the locations of shots taken during the game (even filtering them by player), shows the heatmap of those shots, and allows comparisons with professional players. For this purpose, both alphanumeric data and georeferenced data, such as the positions on the court where specific events occur, are used. Additionally, it collects data from the ACB league website (Asociación de Clubes de Baloncesto) to make comparisons with professional players. The web application consists of two parts: a server implemented in Flask, with a relational database (PostgreSQL), and a web client implemented using Leaflet and HTML. This work followed an agile methodology based on Scrum for project management, with short-duration iterations and incremental review and delivery. Each iteration, known as a sprint, typically has a fixed duration and several stages: planning, development, review, and retrospective. During the sprint, the main objective is to implement a selection of the initially prioritized functionalities, ensuring that each increment works correctly. Meetings with the supervisors are held every two weeks to receive feedback and corrections on the work.

[Abstract]: This paper introduces the TRGST data structure, which is designed to handle queries related to topological relations between paths represented as sequences of stops in a network. As an example, these paths could correspond to stops on a public transport network, and a query of interest is to retrieve paths that share at least k consecutive stops. While topological relations among spatial objects have received extensive attention, the efficient processing of these relations in the context of trajectory paths, considering both time and space efficiency, remains a relatively less explored domain. Taking inspiration from pattern matching implementations, the TRGST data structure is constructed on the foundation of the Generalized Suffix Tree. Its purpose is to provide a compact representation of a set of paths and to efficiently handle topological relation queries by leveraging the pattern search capabilities inherent in this structure. The paper provides a detailed account of the structure and algorithms of TRGST, followed by a performance analysis utilizing both real and synthetic data. The results underscore the remarkable scalability of the TRGST in terms of both query time and space utilization.

[Abstract]: Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first show how a compact representation of quadtrees using O(1) bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy-path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.

[Abstract]: Timber extraction is a vital process in forest harvesting, particularly in areas with high slopes where timber harvesting methods are not feasible. In such cases, logging towers employing extraction cables are often the most effective solution. This intricate task involves several phases, with the installation of the tower being one of the most critical. It significantly influences the performance and feasibility of timber extraction. Another crucial phase involves strategically positioning logging lines to minimize the installation time while maximizing the load capacity efficiency. This article presents an integer programming mathematical model for determining the optimal positioning of yarders conditioned to logging lines, the timber logging time, and the logging cycle time. Furthermore, a two-phase heuristic algorithm is introduced to address the problem. Both approaches offer a preliminary proposal for the location of logging towers and the arrangement of logging lines within a two-dimensional spatial plane, thereby streamlining the timber extraction process in challenging terrains. Finally, we compare manually generated approximate planning (referred to as the manual planning approach, MPA) with our presented approaches. Our methods outperform the MPA, and notably, our two-phase approach surpasses solvers commonly used in the industry by up to 38% in real case studies.

[Abstract]: Among different spatial data models, the topological model for spatial regions explicitly represents common boundaries. This model pursues the efficiency of topology-related queries and the elimination of data redundancy. This paper proposes several space-efficient data structures to support access to the topological representation of two-dimensional regions that are organized in a multi-granular or hierarchical structure, such as the political and administrative partition of a country. In the context of these hierarchies, we focus on queries that search for inclusion, disjointness, and adjacency between regions. The proposed structures build upon compact planar graph embeddings, which show to have a good trade-off between space and time.

[Resumen]: El crecimiento exponencial de los datos en la actualidad plantea desafíos significativos en términos de almacenamiento y procesamiento eficiente de los mismos. Este trabajo fin de grado se centra en la importancia de las estructuras de datos compactas como una solución clave en el tratamiento de datos a gran escala. A diferencia de las técnicas clásicas de compresión, estas estructuras permiten operar con datos sin necesidad de descomprimirlos por completo, lo que ahorra tiempo y espacio en memoria. Este enfoque se ha vuelto esencial en campos como la recuperación de información y la bioinformática debido al crecimiento masivo de datos. El lenguaje de programación Rust, conocido por su seguridad, gestión automática de memoria y eficiencia, se ha convertido en una de las opciones preferidas en la actualidad en términos de innovación y modernización en la industria de la tecnología. Ante la falta de una librería de estructuras de datos compactas en Rust que sea competitiva con el estado del arte en otros lenguajes de programación, este proyecto aprovechará las ventajas que nos proporciona este lenguaje para desarrollar una librería de estructuras de datos compactas de código abierto, proporcionando así a la comunidad científica y a los desarrolladores de Rust una herramienta flexible, potente y fácil de usar para sus proyectos. Además, con este trabajo fin de grado se busca fomentar la reproducibilidad, la reutilización y el avance en la investigación en el campo de investigación en estructuras de datos compactas. De esta manera, se contribuirá a la expansión y adopción de Rust en la investigación y al desarrollo de software científico eficiente y confiable., [Abstract]: The exponential growth of data nowadays poses significant challenges in terms of efficient storage and processing. This undergraduate thesis focuses on the importance of compact data structures as a key solution in handling large scale data. Unlike classical compression techniques, these structures allow for operations on data without the need for complete decompression, saving time and memory space. This approach has become essential in fields such as information retrieval and bioinformatics due to the massive growth of data. The Rust programming language, known for its safety, automatic memory management and efficiency, has become one of the preferred options at present for innovation and modernization in the technology industry. In the absence of a competitive library of compact data structures in Rust compared to the state of the art in other programming languages, this project will leverage the advantages provided by this language to develop an open source compact data structures library. This will provide the scientific community and Rust developers a flexible, powerful, and easy to use tool for their projects. Furthermore, this undergraduate thesis aims to promote reproducibility, reuse, and progress in research on the field of compact data structures. In this way, it will contribute to the expansion and adoption of Rust in research and the development of efficient and reliable scientific software.

To know the overall regional or international scientific production is of vital importance to many areas of knowledge. Nevertheless, in interdisciplinary areas such as Geographic Information Science (GISc) it is not enough to just count papers published in specific journals. Most of them, as is the case of the International Journal of Remote Sensing (IJRS), welcome GISc papers but are not exclusive to that area so the production assignable to authors in the region must consider not only affiliation but also whether or not each paper falls into the theme of GISc. IJRS publishes far more papers than any other GISc journal, so it is important to assess quantitatively how many of them are of GISc. In this work, a representative sample of IJRS articles published over a period of almost 30 years was analyzed using a specific GISc definition. With these data, a manual classification methodology through a set of experts was carried out, and a dataset was built, analyzed, and statistically tested. As a result we estimate that between 47 and 76% of the IJRS articles can be considered from GISc, with a confidence level of 95%. Aside from the primary goal, this set could be used as a gold standard for future classification tasks. It constitutes the first GISc dataset of this kind, that may be used to train artificial intelligence systems capable of performing the same classification automatically and in a massive way. A similar procedure could be applied to other interdisciplinary fields of knowledge as well.