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Además, hay que tener en cuenta que puesto que utilizaré un cluster de Spark, los algoritmos que podremos utilizar estarán limitados por aquellos que se encuentran implementados en la librería MLlib de Apache Spark. También he llevado a cabo un estudio de distintos índices de validación interna y externa que podemos emplear para evaluar la calidad de los grupos o clusters creados por dichos algoritmos. Como caso de uso, he utilizado los datos astrométricos procedentes de millones de estrellas de nuestra galaxia proporcionados por la misión Gaia de la Agencia Espacial Europea para realizar una clusterización de dichas estrellas, con el objetivo de tratar de encontrar cúmulos estelares nuevos o recabar más información sobre los ya existentes. Para llevar a cabo el caso de estudio, dada la gran cantidad de datos a tratar, he utilizado la infraestructura facilitada por la DPAC (Data Processing and Analysis Consortium), consistente en un cluster de Apache Spark formado por 6 nodos con 16 cores y 64Gb de RAM cada uno.0Doctoral Thesis6602<a class="citation_author_name" title="Navegar por nombre de Autor de Tizón Galisteo, Daniel" href="/fez/list/author/Tizón Galisteo, Daniel/">Tizón Galisteo, Daniel</a>. (<span class="citation_date">2017</span>). <i><a class="citation_title" title="Click para ver : Big Data Clustering" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Dtizon">Big Data Clustering</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia ArtificialTizón Galisteo, DanielSarro Baro, Luis Manuelbibliuned:master-ETSInformatica-IAA-Dtizonhttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-DtizonspaTizon_Galisteo_Daniel_TFM.pdfpresmd_Tizon_Galisteo_Daniel_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openaireTizón GalisteoAcceso abierto3.181791534172009-10-09T00:00:00Z6632010-02-17T13:59:05Z2017-05-12T20:53:57ZWeb people searchtesisuned:IngInf-JartilesIn this thesis we have addressed the problem of name ambiguity while searching for people on the Web. At the beginning of our work, in 2004, there were very few research papers on this topic, and no commercial web search engine would provide this type of facility. For this reason, our research methodology initially focused on the design and organisation (together with Prof. Sekine from New York University) of a competitive evaluation campaign for Web People Search systems. Once the campaign had been run for two years, we used the standard test suites built to perform our own empirical studies on the nature and challenges of the task. The evaluation campaign, WePS, was organized in 2007 (as a SemEval 2007 task) and in 2009 (as a WWW 2009 workshop). WePS was crucial in the process to lay the foundations of a proper scientific study of the Web People Search problem. These were the main accomplishments: • Standardisation of the problem: now a majority of researchers focus on the problem as a search results mining task (clustering and information extraction), as it has been defined in WePS. • Creation of standard benchmarks for the task: since the first WePS campaign in 2007, the number of publications related to Web People Search has grown substantially, and most of them use the WePS test suites as a de-facto standard benchmark. As of summer 2009, there were already more than 70 research papers citing WePS overviews; this not only suggests that WePS has indeed become a standard reference for the task, but also that it has contributed to arouse the interest in this kind of research problems. • Design of evaluation metrics for the task: 1. We have performed a careful formal analysis of several extrinsic clustering evaluation metrics based on formal constraints, to conclude that BCubed metrics are the most suitable for the task. We have also extended the original BCubed definition to allow for overlapping clusters, which is a practical requirement of the task. Our results are general enough to be employed in other clustering tasks. 2. We have introduced a new metric combination function, Unanimous Improvement Ratio (UIR), which, unlike Van Rijsbergen’s F, does not require an a-priori weighting of metrics (in our case, BCubed Precision and Recall). In an extensive empirical study we have shown that UIR provides rich information to compare the performance of systems, which was impossible with previous existing metric combinations functions (most prominently F). Using the results of the WePS-2 campaign, we have shown that F and UIR provide complementary information and, altogether, constitute a powerful analytical tool to compare systems. Although we have tested UIR only in the context of our task, it could be potentially useful in any task where several evaluation metrics are needed to capture the quality of a system, as it happens in several Natural Language Processing problems. Using the test suites produced in the two WePS evaluation campaigns, we have then performed a number of empirical studies in order to enhance a better understanding and comprehension of both the nature of the task involved and the way to solve it: • First, we have studied the potential effects of using (interactive) query re- finements to perform the Web People Search task. We have discovered that, although in most occasions there is an expression that can be used as a nearperfect refinement to retrieve all and only those documents referring to an individual, the nature of these ideal refinements is unpredictable and very unlikely to be hypothesized by the user. This confirms the need for search results clustering, and also suggests that looking for an optimal refinement may be a strategy of automatic systems to accomplish the task (and one that has not been used by any participant in the WePS campaigns). • Second, we have studied the usefulness of linguistic (computationally intensive) features as compared to word n-grams and other cheap features to solve our clustering problem. Notably, named entities, which are the most popular feature immediately after bag-of-words approaches, does not seem to provide a direct competitive advantage to solve the task. We have reached this conclusion abstracting from a particular choice of Machine Learning and Text Clustering algorithms, by using a Maximal Pairwise Accuracy estimator introduced in this thesis. • As a side effect of our empirical study, we have built a system which, using the confidence of a binary classifier (whether two pages are coreferent or not) as a similarity metric between document pairs to feed a Hierarchical Agglomerative Clustering algorithm, provides the best results for the task known to us (F0.5 = 0.83 vs. 0.82 for the best WePS-2 system), without using computationally intensive linguistic features.0Doctoral Thesis8312<a class="citation_author_name" title="Navegar por nombre de Autor de Artiles Picón, Javier" href="/fez/list/author/Artiles Picón, Javier/">Artiles Picón, Javier</a>. <b><i><a class="citation_title" title="Click para ver : Web people search" href="/fez/view/tesisuned:IngInf-Jartiles">Web people search</a></i></b> . <span class="citation_date">2009</span>. <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos</span>RecordDoctoral ThesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas InformáticosArtiles Picón, JavierGonzalo Arroyo, Julio (Director de Tesis)Amigó Cabrera, Enrique (Director de Tesis)tesisuned:IngInf-Jartileshttp://e-spacio.uned.es/fez/view/tesisuned:IngInf-JartilesengDocumento.pdfbibliuned:Setthesistesisuned:IngInfbibliuned:Setopenairebibliuned:DptoLSI-ETSI-TesisSet de Tesis Doctorales de la UNEDTesis de la Escuela Técnica Superior de Ingeniería Informática (UNED)Set de openaireDepartamento de Lenguajes y Sistemas Informáticos. E.T.S.I Informática (UNED). Tesishttp://creativecommons.org/licenses/by-nc-nd/4.0Licencia Creative CommonsArtiles PicónAcceso abierto3.024143534232023-06-16T00:00:00Z22024-03-13T06:42:01Z2024-03-13T08:09:44ZGaia Data Release 3. Non-stellar content and source classificationbibliuned:95-Lmsarro-0013Context. As part of the third Gaia Data Release, we present the contributions of the non-stellar and classifcation modules from the eighth coordination unit (CU8) of the Data Processing and Analysis Consortium, which is responsible for the determination of source astrophysical parameters using Gaia data. This is the third in a series of three papers describing the work done within CU8 for this release. Aims. For each of the five relevant modules from CU8, we summarise their objectives, the methods they employ, their performance, and the results they produce for Gaia DR3. We further advise how to use these data products and highlight some limitations. Methods. The Discrete Source Classier (DSC) module provides classification probabilities associated with five types of sources: quasars, galaxies, stars, white dwarfs, and physical binary stars. A subset of these sources are processed by the Outlier Analysis (OA) module, which performs an unsupervised clustering analysis, and then associates labels with the clusters to complement the DSC classfication. The Quasi Stellar Object Classifier (QSOC) and the Unresolved Galaxy Classfier (UGC) determine the redshifts of the sources classfied as quasar and galaxy by the DSC module. Finally, the Total Galactic Extinction (TGE) module uses the extinctions of individual stars determined by another CU8 module to determine the asymptotic extinction along all lines of sight for Galactic latitudes |b| > 5◦. Results. Gaia DR3 includes 1591 million sources with DSC classications; 56 million sources to which the OA clustering is applied; 1.4 million sources with redshift estimates from UGC; 6.4 million sources with QSOC redshift; and 3.1 million level 9 HEALPixes of size 0.013 deg2 where the extinction is evaluated by TGE. Conclusions. Validation shows that results are in good agreement with values from external catalogues; for example 90% of the QSOC redshifts have absolute error lower than 0.1 for sources with empty warning flags, while UGC redshifts have a mean error of 0.008 ± 0.037 if evaluated on a clean set of spectra. An internal validation of the OA results further shows that 30 million sources are located in high confidence regions of the clustering map.0242<a class="citation_author_name" title="Navegar por nombre de Autor de Delchambre, L." href="/fez/list/author/Delchambre, L./">Delchambre, L.</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Bailer-Jones, C.A.L." href="/fez/list/author/Bailer-Jones, C.A.L./">Bailer-Jones, C.A.L.</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Bellas-Velidis, I." href="/fez/list/author/Bellas-Velidis, I./">Bellas-Velidis, I.</a> y <a class="citation_author_name" title="Navegar por nombre de Autor de Sarro Baro, Luis Manuel" href="/fez/list/author/Sarro Baro, Luis Manuel/">Sarro Baro, Luis Manuel</a> . (<span class="citation_date">2023</span>) <a class="citation_title" title="Click para ver : Gaia Data Release 3. Non-stellar content and source classification" href="/fez/view/bibliuned:95-Lmsarro-0013">Gaia Data Release 3. Non-stellar content and source classification</a>. RecordArtículo de revistaPublishedIngeniería InformáticaThe registered version of this paper, first published in Astronomy & Astrophysics (2023) Vol.674, A31, is available online at the publisher's website: EDP Sciences, https://doi.org/10.1051/0004-6361/202243423EDP SciencesDelchambre, L.Bailer-Jones, C.A.L.Bellas-Velidis, I.Sarro Baro, Luis ManuelAstronomy & Astrophysics (A&A)bibliuned:95-Lmsarro-0013http://e-spacio.uned.es/fez/view/bibliuned:95-Lmsarro-0013A31674engSarro_Baro_Luis_Manuel_GDR3_Non_stellar_Content.pdfpresmd_Sarro_Baro_Luis_Manuel_GDR3_Non_stellar_Content.xml1432-0746bibliuned:95bibliuned:Setarticulobibliuned:SetopenaireDepartamento de Inteligencia Artificial (UNED). ArtículosSet de artículoSet de openairehttps://creativecommons.org/licenses/by/4.0/Licencia Creative CommonsDelchambreAcceso abiertohttps://doi.org/10.1051/0004-6361/2022434232.974762734232023-06-16T00:00:00Z232024-02-20T06:09:32Z2024-02-20T06:29:04ZGaia Data Release 3 Chemical cartography of the Milky Waybibliuned:95-Lmsarro-0003Context. The motion of stars has been used to reveal details of the complex history of the Milky Way, in constant interaction with its environment. Nevertheless, to reconstruct the Galactic history puzzle in its entirety, the chemo-physical characterisation of stars is essential. Previous Gaia data releases were supported by a smaller, heterogeneous, and spatially biased mixture of chemical data from ground-based observations. Aims. Gaia Data Release 3 opens a new era of all-sky spectral analysis of stellar populations thanks to the nearly 5.6 million stars observed by the Radial Velocity Spectrometer (RVS) and parametrised by the GSP-Spec module. In this work, we aim to demonstrate the scientific quality of Gaia’s Milky Way chemical cartography through a chemo-dynamical analysis of disc and halo populations. Methods. Stellar atmospheric parameters and chemical abundances provided by Gaia DR3 spectroscopy are combined with DR3 radial velocities and EDR3 astrometry to analyse the relationships between chemistry and Milky Way structure, stellar kinematics, and orbital parameters. Results. The all-sky Gaia chemical cartography allows a powerful and precise chemo-dynamical view of the Milky Way with unprecedented spatial coverage and statistical robustness. First, it reveals the strong vertical symmetry of the Galaxy and the flared structure of the disc. Second, the observed kinematic disturbances of the disc – seen as phase space correlations – and kinematic or orbital substructures are associated with chemical patterns that favour stars with enhanced metallicities and lower [α/Fe] abundance ratios compared to the median values in the radial distributions. This is detected both for young objects that trace the spiral arms and older populations. Several α, iron-peak elements and at least one heavy element trace the thin and thick disc properties in the solar cylinder. Third, young disc stars show a recent chemical impoverishment in several elements. Fourth, the largest chemo-dynamical sample of open clusters analysed so far shows a steepening of the radial metallicity gradient with age, which is also observed in the young field population. Finally, the Gaia chemical data have the required coverage and precision to unveil galaxy accretion debris and heated disc stars on halo orbits through their [α/Fe] ratio, and to allow the study of the chemo-dynamical properties of globular clusters. Conclusions. Gaia DR3 chemo-dynamical diagnostics open new horizons before the era of ground-based wide-field spectroscopic surveys. They unveil a complex Milky Way that is the outcome of an eventful evolution, shaping it to the present day.0292<a class="citation_author_name" title="Navegar por nombre de Autor de Recio-Blanco, A." href="/fez/list/author/Recio-Blanco, A./">Recio-Blanco, A.</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Kordopatis, G." href="/fez/list/author/Kordopatis, G./">Kordopatis, G.</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Laverny, P. de" href="/fez/list/author/Laverny, P. de/">Laverny, P. de</a>, <a class="citation_author_name" title="Navegar por nombre de Autor de Palicio, P.A." href="/fez/list/author/Palicio, P.A./">Palicio, P.A.</a> y <a class="citation_author_name" title="Navegar por nombre de Autor de Sarro Baro, Luis Manuel" href="/fez/list/author/Sarro Baro, Luis Manuel/">Sarro Baro, Luis Manuel</a> . (<span class="citation_date">2023</span>) <a class="citation_title" title="Click para ver : Gaia Data Release 3 Chemical cartography of the Milky Way" href="/fez/view/bibliuned:95-Lmsarro-0003">Gaia Data Release 3 Chemical cartography of the Milky Way</a>. RecordArtículo de revistaPublishedIngeniería InformáticaThe registered version of this paper, first published in Astronomy & Astrophysics (2023) 674, is available online at the publisher's website: Astronomy & Astrophysics; https://doi.org/10.1051/0004-6361/202243511EDP SciencesRecio-Blanco, A.Kordopatis, G.Laverny, P. dePalicio, P.A.Sarro Baro, Luis Manuel1Astronomy & Astrophysics (A&A)bibliuned:95-Lmsarro-0003http://e-spacio.uned.es/fez/view/bibliuned:95-Lmsarro-0003A38674engSarro-baro_luismanuel_GDR3ChemicalCartography.pdfpresmd_Sarro-baro_luismanuel_GDR3ChemicalCartography.xml1432-0746bibliuned:95bibliuned:Setarticulobibliuned:SetopenaireDepartamento de Inteligencia Artificial (UNED). ArtículosSet de artículoSet de openairehttps://creativecommons.org/licenses/by/4.0License Creative CommonsRecio-BlancoAcceso abiertohttps://doi.org/10.1051/0004-6361/2022435112.867462231982012-09-27T00:00:00Z2032021-07-12T22:53:37Z2021-07-12T22:53:37ZEstudio y análisis de las técnicas del pipeline de OCA aplicadas a datos simulados de la misión GAIAbibliuned:master-ETSInformatica-IAA-JgperezEste Trabajo de Fin de Master se enmarca dentro de la mision GAIA de la Agencia Europea del Espacio, cuyo lanzamiento esta previsto para nales de 2013. Debido al elevado volumen de datos que se generara, se ha decidido crear un consorcio para el procesamiento y analisis de datos, llamado Gaia Data Processing and Analysis Consortium (DPAC). Dicho consorcio se encuentra dividido en 10 unidades de coordinacion de las cuales, la octava (CU8), esta enfocada en la estimacion de parametros astrofsicos a partir de las observaciones. El presente trabajo pertenece a los estudios preliminares llevados a cabo dentro del bloque de trabajo Object Clustering Algorithm (OCA) de CU8. El objetivo principal de OCA es desarrollar las herramientas adecuadas para el analisis de los datos recibidos desde la perspectiva del agrupamiento. Los principales retos de OCA son estudiar la tecnicas mas adecuadas de agrupamiento para la naturaleza de los datos de GAIA y ser capaz de escalar dichas tecnicas para poder tratar el gran volumen de datos que se recibira a lo largo de la mision. A lo largo de este trabajo se presentaran las metodologas propuestas en la literatura para poder escalar algoritmos de agrupamiento as como la descripcion de las tecnicas elegidas debido a sus propiedades.0Doctoral Thesis2672<a class="citation_author_name" title="Navegar por nombre de Autor de Pérez Liñana, Juan Gabriel" href="/fez/list/author/Pérez Liñana, Juan Gabriel/">Pérez Liñana, Juan Gabriel</a>. (<span class="citation_date">2012</span>). <i><a class="citation_title" title="Click para ver : Estudio y análisis de las técnicas del pipeline de OCA aplicadas a datos simulados de la misión GAIA" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Jgperez">Estudio y análisis de las técnicas del pipeline de OCA aplicadas a datos simulados de la misión GAIA</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.Pérez Liñana, Juan GabrielSarro Baro, Luis ManuelGarcía Torres, Miguelbibliuned:master-ETSInformatica-IAA-Jgperezhttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-JgperezspaPerez_Linana_JuanGabriel_TFM.pdfpresmd_Perez_Linana_JuanGabriel_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openairePérez LiñanaAcceso abierto2.8082256222252225222252222