Interactive data analysis and its applications on multi-structured datasets

Ph.DDOCTOR OF PHILOSOPH

スカイライン問合わせを利用した大規模データベースの情報選別

Conventional SQL queries take exact input and produce complete result set. However, with massive increase in data volume in different applications, the large result sets returned by traditional SQL queries are not well suited for the users to take effective decisions. Therefore, there is an increasing interest in queries like top-k queries and skyline queries those produce a more concise result set. Top-k queries rely on the scores of the objects to evaluate the usefulness of the objects. In this type of queries, users require to define their own scoring function by combining their interests. Based on the user defined scoring function, the system sorts the objects by their scores and outputs the top-k objects in the ranking list as the result. However, defining a scoring function by the users is a major draw of the top-k queries as in the large data sets where there are many conflicting criteria exist, it is very difficult for the users to define the scoring functions by themselves.……広島大学(Hiroshima University)博士(工学)Engineeringdoctora

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

Optimal Preference Elicitation for Skyline Queries over Categorical Domains

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Painting Pictures with Words - From Theory to System

A picture paints a thousand words, or so we are told. But how many words does it take to paint a picture? And how can words create pictures in the first place? In this thesis we examine a new theory of linguistic meaning -- where the meaning of words and sentences is determined by the scenes they evoke. We describe how descriptive text is parsed and semantically interpreted and how the semantic interpretation is then depicted as a rendered 3D scene. In doing so, we describe WordsEye, our text-to-scene system, and touch upon many fascinating issues of lexical semantics, knowledge representation, and what we call "graphical semantics." We introduce the notion of vignettes as a way to bridge between function and form, between the semantics of language and the grounded semantics of 3D scenes. And we describe how VigNet, our lexical semantic and graphical knowledge base, mediates the whole process. In the second part of this thesis, we describe four different ways WordsEye has been tested. We first discuss an evaluation of the system in an educational environment where WordsEye was shown to significantly improve literacy skills for sixth grade students versus a control group. We then compare WordsEye with Google Image Search on "realistic" and "imaginative" sentences in order to evaluate its performance on a sentence-by-sentence level and test its potential as a way to augment existing image search tools. Thirdly, we describe what we have learned in testing WordsEye as an online 3D authoring system where it has attracted 20,000 real-world users who have performed almost one million scene depictions. Finally, we describe tests of WordsEye as an elicitation tool for field linguists studying endangered languages. We then sum up by presenting a roadmap for enhancing the capabilities of the system and identifying key opportunities and issues to be addressed