Examinando por Autor "Cuesta, Carlos E."

Mostrando 1 - 7 de 7

A Semi-Automatic Data-Scraping Method for the Public Transport Domain
(IEEE Computer Society, 2019-07-31) Vela, Belén; José María, Cavero; Cáceres, Paloma; Cuesta, Carlos E.
The growing amount of data on the Internet has led to a situation in which it is essential to process these data to generate new services with the specific aim of improving people's daily living conditions. Transport data is of the utmost importance, since everyday people have to move around to perform some daily tasks, such as going to work, studying and shopping, and this means that the number of journeys by public transport grows daily. People with special needs make a large number of these trips, but they do not have suficcient information about the accessibility of the routes they want to take. Although there are numerous websites and applications that provide information on public transport services, most do not provide detailed information on the accessibility of the routes. We are, therefore, developing a technological framework for the processing, management, and exploitation of open data to promote accessibility to urban public transport. This is taking place within the framework of the Access@City project. This paper specifically focuses on the data extraction and processing of the existing information on the web concerning public transport and its accessibility for the generation of an open data repository in which to store this information. We, therefore, propose a method for the semi-automatic generation of a data scraper for the public transport domain. This method allows the extraction of public transport data and the existing accessibility information from a selected website. We have additionally developed a web tool that employs the aforementioned method to generate a data scraper for the public transport domain.
An Analysis of Software Parallelism in Big Data Technologies for Data-Intensive Architectures
(Springer Nature, 2021-08-26) Cerezo, Felipe; Cuesta, Carlos E.; Vela, Belén
Data-intensive architectures handle an enormous amount of information, which require the use of big data technologies. These tools include the parallelization mechanisms employed to speed up data processing. However, the increasing volume of these data has an impact on this parallelism and on resource usage. The strategy traditionally employed to increase the processing power has usually been that of adding more resources in order to exploit the parallelism; this strategy is, however, not always feasible in real projects, principally owing to the cost implied. The intention of this paper is, therefore, to analyze how this parallelism can be exploited from a software perspective, focusing specifically on whether big data tools behave as ideally expected: a linear increase in performance with respect to the degree of parallelism and the data load rate. Analysis is consequently carried out of, on the one hand, the impact of the internal data partitioning mechanisms of big data tools and, on the other, the impact on the performance of an increasing data load, while keeping the hardware resources constant. We have, therefore, conducted an experiment with two consolidated big data tools, Kafka and Elasticsearch. Our goal is to analyze the performance obtained when varying the degree of parallelism and the data load rate without ever reaching the limit of hardware resources available. The results of these experiments lead us to conclude that the performance obtained is far from being the ideal speedup, but that software parallelism still has a significant impact.
Architecting Digital Twins Using a Domain-Driven Design-Based Approach
(IEEE Computer Society, 2023-03-13) Macías, Aurora; Navarro, Elena; Cuesta, Carlos E.; Zdun, Uwe
The Digital Twin (DT) concept has overcome its initial definition based on a purely descriptive approach focusing on modelling physical objects, often using CAD. Today DT often describes a behavioural approach that can simulate an object’s dynamics, monitor its state, and control or predict its behaviour. Although DTs are attracting significant attention and offer many advantages in the design of especially cyber-physical systems, most proposals have focused on developing DTs for a specific use case or need without providing a more holistic approach to its design. We aim to propose a domain-agnostic approach for architecting DTs. Here, DTs are directly supported by Domain-Driven Design’s notion of Bounded Contexts (BCs), hiding all the domain-inherent specifications behind BC boundaries. These BCs are also the central abstraction in many microservice architectures and can be used to describe DTs. A Wind Turbine DT architecture is used as a running example to describe how every relevant DT property can be satisfied following our proposal for architecting digital twins. A qualitative evaluation of this case by five external practitioners shows that our DDD-based proposal consistently outperforms the 5-dimension model used as the reference approach.
Improving Urban Mobility by Defining a Smart Data Integration Platform
(IEEE Computer Society, 2020-10-26) Cáceres, Paloma; Sierra-Alonso, Almudena; Cuesta, Carlos E.; Vela, Belén; Cavero Barca, José María
One of the key factors employed to define the well-being of citizens in the urban environment is mobility, since it defines a set of flows and connections that constrain those citizens' individual and collective behaviour. However, the complexity of this activity on the scale of a city makes this a complex problem in computational terms. One of the main reasons for this is the asymmetry of information: different actors have access only to partial or outdated information, and many relevant data are simply unavailable. In this article, we propose a data integration architecture and platform with which to combine relevant data from many different sources and provide the results in a variety of forms. This integration uses semantic technologies, thus ensuring that the relationships among data show their actual meaning and are appropriately interpreted. The resulting platform amalgamates: open data, which is available from public sources; extracted data, obtained from public sites by means of scraping techniques; pre-processed data, stored in public databases;aggregated data, acquired from pervasive devices by means of crowdsourcing; smart data, supplied by mobile applications and enriched with contextual information, or data concerning specific incidents, often provided by the users themselves. The semantic integration of this information makes it possible to compute a wide range of results, from accessible transport routes to identifiable events, in a coordinated manner. The general public is then supplied with these results through the use of specific software, via either mobile applications or the web. We are of the opinion that the collective use of this information may improve urban welfare.
Innóvil: una aplicación para facilitar la coordinación y comunicación entre profesores y alumnos
(2014-03-22) Vela, Belén; Garrido, Miguel A.; Cavero, José María; Cáceres, Paloma; Cuesta, Carlos E.; Sierra-Alonso, Almudena
En este trabajo presentamos Innóvil, una aplicación para dispositivos móviles que ofrece dos servicios orientados a facilitar la coordinación y comunicación entre alumnos y profesores: por una parte un servicio de calendario colaborativo que permite a los profesores de una misma titulación y curso coordinar pruebas y prácticas de los alumnos; por otra parte, un servicio de foro para facilitar los debates de los temas planteados por el profesor en sus asignaturas. Los servicios de esta aplicación se han probado, como experiencia piloto, en tres asignaturas de tres titulaciones de la Escuela Técnica Superior de Ingeniería Informática.
Smart data at play: improving accessibility in the urban transport system
(Taylor and Francis, 2019-08-16) Cáceres, Paloma; Cuesta, Carlos E.; Vela, Belén; Cavero, José María; Sierra, Almudena
Human mobility is one of the most important concerns in smart city initiatives and is especially relevant when combined with accessibility issues. This paper describes work in the context of the Access@City Research Project, which seeks to improve the accessibility in the public transport system by using available information (open data, semantic-aware knowledge) provided by transport organizations. However, these organizations provide partial data, and a lot of information is still available only on their websites, or simply does not exist. This absence can be tackled using a playful approach - the use of gaming apps to obtain and update accessibility information. In this paper, we describe the use of a hybrid reality game (HRG) to enrich information regarding the accessibility of subway stations. In turn, the player improves her score, which is included as part of the game. The correlation between these observations is able to provide, in a relatively short time, an accurate description of the accessibility of these stations. In summary, this playful approach makes it possible to recover a set of accessibility data that ultimately provide these smart capabilities, which are the core of this “accessible city” endeavour.
SOLID: una Arquitectura para la Gestión de Big Semantic Data en Tiempo Real
(2014-02-04) Arias, Mario; Cuesta, Carlos E.; Fernández, Javier D.; Martínez-Prieto, Miguel Angel
La gestión de grandes colecciones de datos (Big Data) es un proceso crítico en entornos de explotación en tiempo real ya que las arquitecturas batch, que garantizan un comportamiento escalable, ofrecen unos tiempos de respuesta insuficientes para los requisitos de rendimiento que se presentan en dichos entornos. En este artículo se estudia esta problemática, de acuerdo a las necesidades planteadas por aquellos sistemas de información en los que se forman y exponen grandes colecciones de RDF (Big Semantic Data) en tiempo real. Nuestra propuesta es una nueva arquitectura (SOLID) que aísla la complejidad de almacenar grandes colecciones de datos y las necesidades específicas de insertar y consultar Big Semantic Data en tiempo real. La base tecnológica de SOLID comprende el uso de RDF/HDT para el almacenamiento auto-indexado de los datos y tecnología NoSQL para su gestión en tiempo real. Nuestros resultados experimentales muestran la eficiencia de cada una de las capas de datos y su integración mediante dos capas software adicionales que garantizan la escalabilidad de SOLID .