Behavior of Socket Base Connections Emphasizing Pedestal Walls

To evaluate the main design models for socket base connections of precast concrete structures, an experimental investigation was carried out on specimens of this connection with smooth and rough interfaces in contact with cast-in-place concrete. The specimens consisted of pedestal walls and were submitted to loads with large eccentricities. Based on the experimental results, two rational design models are proposed for this connection. One of these models accounts for the friction and is applied to socket bases with smooth interfaces. The main behavior model was verified for sockets with this type of interface and the design of the longitudinal walls as corbels is also suggested in this case. Because the behavior of the rough interface specimens was very close to a monolithic connection, the other proposed model is an adaptation of the bending theory to calculate the vertical reinforcement of socket bases with rough interfaces.

Characterization of rat behavior in the elevated plus-maze using a directed graph

The elevated plus-maze is a device widely used to assess rodent anxiety under the effect of several treatments, including pharmacological agents. The animal is placed at the center of the apparatus, which consists of two open arms and two arms enclosed by walls, and the number of entries and duration of stay in each arm are measured for a 5-min exposure period. The effect of an anxiolytic drug is to increase the percentage of time spent and number of entries into the open arms. In this work, we propose a new measure of anxiety levels in the rat submitted to the elevated plus-maze. We represented the spatial structure of the elevated plus-maze in terms of a directed graph and studied the statistics of the rat`s transitions between the nodes of the graph. By counting the number of times each transition is made and ordering them in descending frequency we represented the rat`s behavior in a rank-frequency plot. Our results suggest that the curves obtained under different pharmacological conditions can be well fitted by a power law with an exponent sensitive to both the drug type and the dose used. (C) 2009 Elsevier B.V. All rights reserved.

Characterization of the rat exploratory behavior in the elevated plus-maze with Markov chains

The elevated plus-maze is an animal model of anxiety used to study the effect of different drugs on the behavior of the animal It consists of a plus-shaped maze with two open and two closed arms elevated 50 cm from the floor The standard measures used to characterize exploratory behavior in the elevated plus-maze are the time spent and the number of entries in the open arms In this work we use Markov chains to characterize the exploratory behavior of the rat in the elevated plus-maze under three different conditions normal and under the effects of anxiogenic and anxiolytic drugs The spatial structure of the elevated plus-maze is divided into squares which are associated with states of a Markov chain By counting the frequencies of transitions between states during 5-min sessions in the elevated plus-maze we constructed stochastic matrices for the three conditions studied The stochastic matrices show specific patterns which correspond to the observed behaviors of the rat under the three different conditions For the control group the stochastic matrix shows a clear preference for places in the closed arms This preference is enhanced for the anxiogenic group For the anxiolytic group the stochastic matrix shows a pattern similar to a random walk Our results suggest that Markov chains can be used together with the standard measures to characterize the rat behavior in the elevated plus-maze (C) 2010 Elsevier B V All rights reserved

Algorithms and methodologies for decision support in energy efficiency on buildings

Dissertação apresentada na faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

MORM--a Petri net based model for assessing OH&S risks in industrial processes: modeling qualitative aspects.

Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets to perform multiple-oriented analysis opens perspectives in the field of industrial risk management. It may significantly reduce the duration of the assessment process. But, most of all, it opens perspectives in the field of risk comparisons and integrated risk management. Moreover, because of the generic nature of the model and tool used, the same concepts and patterns may be used to model a wide range of systems and application fields.

Are Evaluation of ICTs Investment Using Decision Support Systems:Case Applications from Distributor's and End User's Perspective Group Decision

The evaluation of investments in advanced technology is one of the most important decision making tasks. The importance is even more pronounced considering the huge budget concerning the strategic, economic and analytic justification in order to shorten design and development time. Choosing the most appropriate technology requires an accurate and reliable system that can lead the decision makers to obtain such a complicated task. Currently, several Information and Communication Technologies (ICTs) manufacturers that design global products are seeking local firms to act as their sales and services representatives (called distributors) to the end user. At the same time, the end user or customer is also searching for the best possible deal for their investment in ICT's projects. Therefore, the objective of this research is to present a holistic decision support system to assist the decision maker in Small and Medium Enterprises (SMEs) - working either as individual decision makers or in a group - in the evaluation of the investment to become an ICT's distributor or an ICT's end user. The model is composed of the Delphi/MAH (Maximising Agreement Heuristic) Analysis, a well-known quantitative method in Group Support System (GSS), which is applied to gather the average ranking data from amongst Decision Makers (DMs). After that the Analytic Network Process (ANP) analysis is brought in to analyse holistically: it performs quantitative and qualitative analysis simultaneously. The illustrative data are obtained from industrial entrepreneurs by using the Group Support System (GSS) laboratory facilities at Lappeenranta University of Technology, Finland and in Thailand. The result of the research, which is currently implemented in Thailand, can provide benefits to the industry in the evaluation of becoming an ICT's distributor or an ICT's end user, particularly in the assessment of the Enterprise Resource Planning (ERP) programme. After the model is put to test with an in-depth collaboration with industrial entrepreneurs in Finland and Thailand, the sensitivity analysis is also performed to validate the robustness of the model. The contribution of this research is in developing a new approach and the Delphi/MAH software to obtain an analysis of the value of becoming an ERP distributor or end user that is flexible and applicable to entrepreneurs, who are looking for the most appropriate investment to become an ERP distributor or end user. The main advantage of this research over others is that the model can deliver the value of becoming an ERP distributor or end user in a single number which makes it easier for DMs to choose the most appropriate ERP vendor. The associated advantage is that the model can include qualitative data as well as quantitative data, as the results from using quantitative data alone can be misleading and inadequate. There is a need to utilise quantitative and qualitative analysis together, as can be seen from the case studies.

Modeling building semantics: providing feedback and sustainability

Even minor changes in user activity can bring about significant energy savings within built space. Many building performance assessment methods have been developed, however these often disregard the impact of user behavior (i.e. the social, cultural and organizational aspects of the building). Building users currently have limited means of determining how sustainable they are, in context of the specific building structure and/or when compared to other users performing similar activities, it is therefore easy for users to dismiss their energy use. To support sustainability, buildings must be able to monitor energy use, identify areas of potential change in the context of user activity and provide contextually relevant information to facilitate persuasion management. If the building is able to provide users with detailed information about how specific user activity that is wasteful, this should provide considerable motivation to implement positive change. This paper proposes using a dynamic and temporal semantic model, to populate information within a model of persuasion, to manage user change. By semantically mapping a building, and linking this to persuasion management we suggest that: i) building energy use can be monitored and analyzed over time; ii) persuasive management can be facilitated to move user activity towards sustainability.

Moral hazard and nonlinear pricing in a general equilibrium model

The paper analyzes a two period general equilibrium model with individual risk and moral hazard. Each household faces two individual states of nature in the second period. These states solely differ in the household's vector of initial endowments, which is strictly larger in the first state (good state) than in the second state (bad state). In the first period households choose a non-observable action. Higher leveis of action give higher probability of the good state of nature to occur, but lower leveIs of utility. Households have access to an insurance market that allows transfer of income across states of oature. I consider two models of financiaI markets, the price-taking behavior model and the nonlínear pricing modelo In the price-taking behavior model suppliers of insurance have a belief about each household's actíon and take asset prices as given. A variation of standard arguments shows the existence of a rational expectations equilibrium. For a generic set of economies every equilibrium is constraíned sub-optímal: there are commodity prices and a reallocation of financiaI assets satisfying the first period budget constraint such that, at each household's optimal choice given those prices and asset reallocation, markets clear and every household's welfare improves. In the nonlinear pricing model suppliers of insurance behave strategically offering nonlinear pricing contracts to the households. I provide sufficient conditions for the existence of equilibrium and investigate the optimality properties of the modeI. If there is a single commodity then every equilibrium is constrained optimaI. Ir there is more than one commodity, then for a generic set of economies every equilibrium is constrained sub-optimaI.

A Simple maxwell Based Model in Order to Represent the frequency-dependent viscosity measured by ultrasound

A previous work showed that viscosity values measured high frequency by ultrasound agreed with the values at low frequency by the rotational viscometer when conditions are met, such as relatively low frequency viscosity. However, these conditions strongly reduce the range of the measurement cell. In order to obtain a measurement range and sensitivity high frequency must used, but it causes a frequency-dependent decrease on the viscosity values. This work introduces a new simple in order to represent this frequency-dependent behavior.model is based on the Maxwell model for viscoelastic , but using a variable parameter. This parameter has physical meaning because it represents the linear behavior the apparent elasticity measured along with the viscosity by .Automotive oils SAE 90 and SAE 250 at 22.5±0.5oC viscosities at low frequency of 0.6 and 6.7 Pa.s, respectively,tested in the range of 1-5 MHz. The model was used in to fit the obtained data using an algorithm of non-linear in Matlab. By including the viscosity at low frequency an unknown fitting parameter, it is possible to extrapolate its . Relative deviations between the values measured by the and extrapolated using the model for the SAE 90 and SAE 250 oils were 5.0% and 15.7%, respectively.©2008 IEEE.

A interação do usuário com catálogos bibliográficos on-line: investigação a partir da Teoria Fundamentada

Pós-graduação em Ciência da Informação - FFC

Gamificação aplicada à produção de roteiro para audiovisual educativo interativo

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach.

Análisis y modelización de la dinámica emergente durante el proceso de difusión de información en las redes sociales de internet

Durante la actividad diaria, la sociedad actual interactúa constantemente por medio de dispositivos electrónicos y servicios de telecomunicaciones, tales como el teléfono, correo electrónico, transacciones bancarias o redes sociales de Internet. Sin saberlo, masivamente dejamos rastros de nuestra actividad en las bases de datos de empresas proveedoras de servicios. Estas nuevas fuentes de datos tienen las dimensiones necesarias para que se puedan observar patrones de comportamiento humano a grandes escalas. Como resultado, ha surgido una reciente explosión sin precedentes de estudios de sistemas sociales, dirigidos por el análisis de datos y procesos computacionales. En esta tesis desarrollamos métodos computacionales y matemáticos para analizar sistemas sociales por medio del estudio combinado de datos derivados de la actividad humana y la teoría de redes complejas. Nuestro objetivo es caracterizar y entender los sistemas emergentes de interacciones sociales en los nuevos espacios tecnológicos, tales como la red social Twitter y la telefonía móvil. Analizamos los sistemas por medio de la construcción de redes complejas y series temporales, estudiando su estructura, funcionamiento y evolución en el tiempo. También, investigamos la naturaleza de los patrones observados por medio de los mecanismos que rigen las interacciones entre individuos, así como medimos el impacto de eventos críticos en el comportamiento del sistema. Para ello, hemos propuesto modelos que explican las estructuras globales y la dinámica emergente con que fluye la información en el sistema. Para los estudios de la red social Twitter, hemos basado nuestros análisis en conversaciones puntuales, tales como protestas políticas, grandes acontecimientos o procesos electorales. A partir de los mensajes de las conversaciones, identificamos a los usuarios que participan y construimos redes de interacciones entre los mismos. Específicamente, construimos una red para representar quién recibe los mensajes de quién y otra red para representar quién propaga los mensajes de quién. En general, hemos encontrado que estas estructuras tienen propiedades complejas, tales como crecimiento explosivo y distribuciones de grado libres de escala. En base a la topología de estas redes, hemos indentificado tres tipos de usuarios que determinan el flujo de información según su actividad e influencia. Para medir la influencia de los usuarios en las conversaciones, hemos introducido una nueva medida llamada eficiencia de usuario. La eficiencia se define como el número de retransmisiones obtenidas por mensaje enviado, y mide los efectos que tienen los esfuerzos individuales sobre la reacción colectiva. Hemos observado que la distribución de esta propiedad es ubicua en varias conversaciones de Twitter, sin importar sus dimensiones ni contextos. Con lo cual, sugerimos que existe universalidad en la relación entre esfuerzos individuales y reacciones colectivas en Twitter. Para explicar los factores que determinan la emergencia de la distribución de eficiencia, hemos desarrollado un modelo computacional que simula la propagación de mensajes en la red social de Twitter, basado en el mecanismo de cascadas independientes. Este modelo nos permite medir el efecto que tienen sobre la distribución de eficiencia, tanto la topología de la red social subyacente, como la forma en que los usuarios envían mensajes. Los resultados indican que la emergencia de un grupo selecto de usuarios altamente eficientes depende de la heterogeneidad de la red subyacente y no del comportamiento individual. Por otro lado, hemos desarrollado técnicas para inferir el grado de polarización política en redes sociales. Proponemos una metodología para estimar opiniones en redes sociales y medir el grado de polarización en las opiniones obtenidas. Hemos diseñado un modelo donde estudiamos el efecto que tiene la opinión de un pequeño grupo de usuarios influyentes, llamado élite, sobre las opiniones de la mayoría de usuarios. El modelo da como resultado una distribución de opiniones sobre la cual medimos el grado de polarización. Aplicamos nuestra metodología para medir la polarización en redes de difusión de mensajes, durante una conversación en Twitter de una sociedad políticamente polarizada. Los resultados obtenidos presentan una alta correspondencia con los datos offline. Con este estudio, hemos demostrado que la metodología propuesta es capaz de determinar diferentes grados de polarización dependiendo de la estructura de la red. Finalmente, hemos estudiado el comportamiento humano a partir de datos de telefonía móvil. Por una parte, hemos caracterizado el impacto que tienen desastres naturales, como innundaciones, sobre el comportamiento colectivo. Encontramos que los patrones de comunicación se alteran de forma abrupta en las áreas afectadas por la catástofre. Con lo cual, demostramos que se podría medir el impacto en la región casi en tiempo real y sin necesidad de desplegar esfuerzos en el terreno. Por otra parte, hemos estudiado los patrones de actividad y movilidad humana para caracterizar las interacciones entre regiones de un país en desarrollo. Encontramos que las redes de llamadas y trayectorias humanas tienen estructuras de comunidades asociadas a regiones y centros urbanos. En resumen, hemos mostrado que es posible entender procesos sociales complejos por medio del análisis de datos de actividad humana y la teoría de redes complejas. A lo largo de la tesis, hemos comprobado que fenómenos sociales como la influencia, polarización política o reacción a eventos críticos quedan reflejados en los patrones estructurales y dinámicos que presentan la redes construidas a partir de datos de conversaciones en redes sociales de Internet o telefonía móvil. ABSTRACT During daily routines, we are constantly interacting with electronic devices and telecommunication services. Unconsciously, we are massively leaving traces of our activity in the service providers’ databases. These new data sources have the dimensions required to enable the observation of human behavioral patterns at large scales. As a result, there has been an unprecedented explosion of data-driven social research. In this thesis, we develop computational and mathematical methods to analyze social systems by means of the combined study of human activity data and the theory of complex networks. Our goal is to characterize and understand the emergent systems from human interactions on the new technological spaces, such as the online social network Twitter and mobile phones. We analyze systems by means of the construction of complex networks and temporal series, studying their structure, functioning and temporal evolution. We also investigate on the nature of the observed patterns, by means of the mechanisms that rule the interactions among individuals, as well as on the impact of critical events on the system’s behavior. For this purpose, we have proposed models that explain the global structures and the emergent dynamics of information flow in the system. In the studies of the online social network Twitter, we have based our analysis on specific conversations, such as political protests, important announcements and electoral processes. From the messages related to the conversations, we identify the participant users and build networks of interactions with them. We specifically build one network to represent whoreceives- whose-messages and another to represent who-propagates-whose-messages. In general, we have found that these structures have complex properties, such as explosive growth and scale-free degree distributions. Based on the topological properties of these networks, we have identified three types of user behavior that determine the information flow dynamics due to their influence. In order to measure the users’ influence on the conversations, we have introduced a new measure called user efficiency. It is defined as the number of retransmissions obtained by message posted, and it measures the effects of the individual activity on the collective reacixtions. We have observed that the probability distribution of this property is ubiquitous across several Twitter conversation, regardlessly of their dimension or social context. Therefore, we suggest that there is a universal behavior in the relationship between individual efforts and collective reactions on Twitter. In order to explain the different factors that determine the user efficiency distribution, we have developed a computational model to simulate the diffusion of messages on Twitter, based on the mechanism of independent cascades. This model, allows us to measure the impact on the emergent efficiency distribution of the underlying network topology, as well as the way that users post messages. The results indicate that the emergence of an exclusive group of highly efficient users depends upon the heterogeneity of the underlying network instead of the individual behavior. Moreover, we have also developed techniques to infer the degree of polarization in social networks. We propose a methodology to estimate opinions in social networks and to measure the degree of polarization in the obtained opinions. We have designed a model to study the effects of the opinions of a small group of influential users, called elite, on the opinions of the majority of users. The model results in an opinions distribution to which we measure the degree of polarization. We apply our methodology to measure the polarization on graphs from the messages diffusion process, during a conversation on Twitter from a polarized society. The results are in very good agreement with offline and contextual data. With this study, we have shown that our methodology is capable of detecting several degrees of polarization depending on the structure of the networks. Finally, we have also inferred the human behavior from mobile phones’ data. On the one hand, we have characterized the impact of natural disasters, like flooding, on the collective behavior. We found that the communication patterns are abruptly altered in the areas affected by the catastrophe. Therefore, we demonstrate that we could measure the impact of the disaster on the region, almost in real-time and without needing to deploy further efforts. On the other hand, we have studied human activity and mobility patterns in order to characterize regional interactions on a developing country. We found that the calls and trajectories networks present community structure associated to regional and urban areas. In summary, we have shown that it is possible to understand complex social processes by means of analyzing human activity data and the theory of complex networks. Along the thesis, we have demonstrated that social phenomena, like influence, polarization and reaction to critical events, are reflected in the structural and dynamical patterns of the networks constructed from data regarding conversations on online social networks and mobile phones.

Arquitectura y modelo de referencia para un ecosistema de intercambio de datos personales centrado en el usuario

Los servicios telemáticos han transformando la mayoría de nuestras actividades cotidianas y ofrecen oportunidades sin precedentes con características como, por ejemplo, el acceso ubicuo, la disponibilidad permanente, la independencia del dispositivo utilizado, la multimodalidad o la gratuidad, entre otros. No obstante, los beneficios que destacan en cuanto se reflexiona sobre estos servicios, tienen como contrapartida una serie de riesgos y amenazas no tan obvios, ya que éstos se nutren de y tratan con datos personales, lo cual suscita dudas respecto a la privacidad de las personas. Actualmente, las personas que asumen el rol de usuarios de servicios telemáticos generan constantemente datos digitales en distintos proveedores. Estos datos reflejan parte de su intimidad, de sus características particulares, preferencias, intereses, relaciones sociales, hábitos de consumo, etc. y lo que es más controvertido, toda esta información se encuentra bajo la custodia de distintos proveedores que pueden utilizarla más allá de las necesidades y el control del usuario. Los datos personales y, en particular, el conocimiento sobre los usuarios que se puede extraer a partir de éstos (modelos de usuario) se han convertido en un nuevo activo económico para los proveedores de servicios. De este modo, estos recursos se pueden utilizar para ofrecer servicios centrados en el usuario basados, por ejemplo, en la recomendación de contenidos, la personalización de productos o la predicción de su comportamiento, lo cual permite a los proveedores conectar con los usuarios, mantenerlos, involucrarlos y en definitiva, fidelizarlos para garantizar el éxito de un modelo de negocio. Sin embargo, dichos recursos también pueden utilizarse para establecer otros modelos de negocio que van más allá de su procesamiento y aplicación individual por parte de un proveedor y que se basan en su comercialización y compartición con otras entidades. Bajo esta perspectiva, los usuarios sufren una falta de control sobre los datos que les refieren, ya que esto depende de la voluntad y las condiciones impuestas por los proveedores de servicios, lo cual implica que habitualmente deban enfrentarse ante la disyuntiva de ceder sus datos personales o no acceder a los servicios telemáticos ofrecidos. Desde el sector público se trata de tomar medidas que protejan a los usuarios con iniciativas y legislaciones que velen por su privacidad y que aumenten el control sobre sus datos personales, a la vez que debe favorecer el desarrollo económico propiciado por estos proveedores de servicios. En este contexto, esta tesis doctoral propone una arquitectura y modelo de referencia para un ecosistema de intercambio de datos personales centrado en el usuario que promueve la creación, compartición y utilización de datos personales y modelos de usuario entre distintos proveedores, al mismo tiempo que ofrece a los usuarios las herramientas necesarias para ejercer su control en cuanto a la cesión y uso de sus recursos personales y obtener, en su caso, distintos incentivos o contraprestaciones económicas. Las contribuciones originales de la tesis son la especificación y diseño de una arquitectura que se apoya en un proceso de modelado distribuido que se ha definido en el marco de esta investigación. Éste se basa en el aprovechamiento de recursos que distintas entidades (fuentes de datos) ofrecen para generar modelos de usuario enriquecidos que cubren las necesidades específicas de terceras entidades, considerando la participación del usuario y el control sobre sus recursos personales (datos y modelos de usuario). Lo anterior ha requerido identificar y caracterizar las fuentes de datos con potencial de abastecer al ecosistema, determinar distintos patrones para la generación de modelos de usuario a partir de datos personales distribuidos y heterogéneos y establecer una infraestructura para la gestión de identidad y privacidad que permita a los usuarios expresar sus preferencias e intereses respecto al uso y compartición de sus recursos personales. Además, se ha definido un modelo de negocio de referencia que sustenta las investigaciones realizadas y que ha sido particularizado en dos ámbitos de aplicación principales, en concreto, el sector de publicidad en redes sociales y el sector financiero para la implantación de nuevos servicios. Finalmente, cabe destacar que las contribuciones de esta tesis han sido validadas en el contexto de distintos proyectos de investigación industrial aplicada y también en el marco de proyectos fin de carrera que la autora ha tutelado o en los que ha colaborado. Los resultados obtenidos han originado distintos méritos de investigación como dos patentes en explotación, la publicación de un artículo en una revista con índice de impacto y diversos artículos en congresos internacionales de relevancia. Algunos de éstos han sido galardonados con premios de distintas instituciones, así como en las conferencias donde han sido presentados. ABSTRACT Information society services have changed most of our daily activities, offering unprecedented opportunities with certain characteristics, such as: ubiquitous access, permanent availability, device independence, multimodality and free-of-charge services, among others. However, all the positive aspects that emerge when thinking about these services have as counterpart not-so-obvious threats and risks, because they feed from and use personal data, thus creating concerns about peoples’ privacy. Nowadays, people that play the role of user of services are constantly generating digital data in different service providers. These data reflect part of their intimacy, particular characteristics, preferences, interests, relationships, consumer behavior, etc. Controversy arises because this personal information is stored and kept by the mentioned providers that can use it beyond the user needs and control. Personal data and, in particular, the knowledge about the user that can be obtained from them (user models) have turned into a new economic asset for the service providers. In this way, these data and models can be used to offer user centric services based, for example, in content recommendation, tailored-products or user behavior, all of which allows connecting with the users, keeping them more engaged and involved with the provider, finally reaching customer loyalty in order to guarantee the success of a business model. However, these resources can be used to establish a different kind of business model; one that does not only processes and individually applies personal data, but also shares and trades these data with other entities. From that perspective, the users lack control over their referred data, because it depends from the conditions imposed by the service providers. The consequence is that the users often face the following dilemma: either giving up their personal data or not using the offered services. The Public Sector takes actions in order to protect the users approving, for example, laws and legal initiatives that reinforce privacy and increase control over personal data, while at the same time the authorities are also key players in the economy development that derives from the information society services. In this context, this PhD Dissertation proposes an architecture and reference model to achieve a user-centric personal data ecosystem that promotes the creation, sharing and use of personal data and user models among different providers, while offering users the tools to control who can access which data and why and if applicable, to obtain different incentives. The original contributions obtained are the specification and design of an architecture that supports a distributed user modelling process defined by this research. This process is based on leveraging scattered resources of heterogeneous entities (data sources) to generate on-demand enriched user models that fulfill individual business needs of third entities, considering the involvement of users and the control over their personal resources (data and user models). This has required identifying and characterizing data sources with potential for supplying resources, defining different generation patterns to produce user models from scattered and heterogeneous data, and establishing identity and privacy management infrastructures that allow users to set their privacy preferences regarding the use and sharing of their resources. Moreover, it has also been proposed a reference business model that supports the aforementioned architecture and this has been studied for two application fields: social networks advertising and new financial services. Finally, it has to be emphasized that the contributions obtained in this dissertation have been validated in the context of several national research projects and master thesis that the author has directed or has collaborated with. Furthermore, these contributions have produced different scientific results such as two patents and different publications in relevant international conferences and one magazine. Some of them have been awarded with different prizes.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.