CumuloNimbo: A highly-scalable transaction processing platform as a service.

One of the main challenges facing next generation Cloud platform services is the need to simultaneously achieve ease of programming, consistency, and high scalability. Big Data applications have so far focused on batch processing. The next step for Big Data is to move to the online world. This shift will raise the requirements for transactional guarantees. CumuloNimbo is a new EC-funded project led by Universidad Politécnica de Madrid (UPM) that addresses these issues via a highly scalable multi-tier transactional platform as a service (PaaS) that bridges the gap between OLTP and Big Data applications.

A big data platform for large scale event processing

To date, big data applications have focused on the store-and-process paradigm. In this paper we describe an initiative to deal with big data applications for continuous streams of events. In many emerging applications, the volume of data being streamed is so large that the traditional ‘store-then-process’ paradigm is either not suitable or too inefficient. Moreover, soft-real time requirements might severely limit the engineering solutions. Many scenarios fit this description. In network security for cloud data centres, for instance, very high volumes of IP packets and events from sensors at firewalls, network switches and routers and servers need to be analyzed and should detect attacks in minimal time, in order to limit the effect of the malicious activity over the IT infrastructure. Similarly, in the fraud department of a credit card company, payment requests should be processed online and need to be processed as quickly as possible in order to provide meaningful results in real-time. An ideal system would detect fraud during the authorization process that lasts hundreds of milliseconds and deny the payment authorization, minimizing the damage to the user and the credit card company.

Area-delay Trade-offs of Texture Decompressors for a Graphics Processing Unit

Graphics Processing Units have become a booster for the microelectronics industry. However, due to intellectual property issues, there is a serious lack of information on implementation details of the hardware architecture that is behind GPUs. For instance, the way texture is handled and decompressed in a GPU to reduce bandwidth usage has never been dealt with in depth from a hardware point of view. This work addresses a comparative study on the hardware implementation of different texture decompression algorithms for both conventional (PCs and video game consoles) and mobile platforms. Circuit synthesis is performed targeting both a reconfigurable hardware platform and a 90nm standard cell library. Area-delay trade-offs have been extensively analyzed, which allows us to compare the complexity of decompressors and thus determine suitability of algorithms for systems with limited hardware resources.

A model to develop a platform for personalised health applications

Antecedentes Europa vive una situación insostenible. Desde el 2008 se han reducido los recursos de los gobiernos a raíz de la crisis económica. El continente Europeo envejece con ritmo constante al punto que se prevé que en 2050 habrá sólo dos trabajadores por jubilado [54]. A esta situación se le añade el aumento de la incidencia de las enfermedades crónicas, relacionadas con el envejecimiento, cuyo coste puede alcanzar el 7% del PIB de un país [51]. Es necesario un cambio de paradigma. Una nueva manera de cuidar de la salud de las personas: sustentable, eficaz y preventiva más que curativa. Algunos estudios abogan por el cuidado personalizado de la salud (pHealth). En este modelo las prácticas médicas son adaptadas e individualizadas al paciente, desde la detección de los factores de riesgo hasta la personalización de los tratamientos basada en la respuesta del individuo [81]. El cuidado personalizado de la salud está asociado a menudo al uso de las tecnologías de la información y comunicación (TICs) que, con su desarrollo exponencial, ofrecen oportunidades interesantes para la mejora de la salud. El cambio de paradigma hacia el pHealth está lentamente ocurriendo, tanto en el ámbito de la investigación como en la industria, pero todavía no de manera significativa. Existen todavía muchas barreras relacionadas a la economía, a la política y la cultura. También existen barreras puramente tecnológicas, como la falta de sistemas de información interoperables [199]. A pesar de que los aspectos de interoperabilidad están evolucionando, todavía hace falta un diseño de referencia especialmente direccionado a la implementación y el despliegue en gran escala de sistemas basados en pHealth. La presente Tesis representa un intento de organizar la disciplina de la aplicación de las TICs al cuidado personalizado de la salud en un modelo de referencia, que permita la creación de plataformas de desarrollo de software para simplificar tareas comunes de desarrollo en este dominio. Preguntas de investigación RQ1 >Es posible definir un modelo, basado en técnicas de ingeniería del software, que represente el dominio del cuidado personalizado de la salud de una forma abstracta y representativa? RQ2 >Es posible construir una plataforma de desarrollo basada en este modelo? RQ3 >Esta plataforma ayuda a los desarrolladores a crear sistemas pHealth complejos e integrados? Métodos Para la descripción del modelo se adoptó el estándar ISO/IEC/IEEE 42010por ser lo suficientemente general y abstracto para el amplio enfoque de esta tesis [25]. El modelo está definido en varias partes: un modelo conceptual, expresado a través de mapas conceptuales que representan las partes interesadas (stakeholders), los artefactos y la información compartida; y escenarios y casos de uso para la descripción de sus funcionalidades. El modelo fue desarrollado de acuerdo a la información obtenida del análisis de la literatura, incluyendo 7 informes industriales y científicos, 9 estándares, 10 artículos en conferencias, 37 artículos en revistas, 25 páginas web y 5 libros. Basándose en el modelo se definieron los requisitos para la creación de la plataforma de desarrollo, enriquecidos por otros requisitos recolectados a través de una encuesta realizada a 11 ingenieros con experiencia en la rama. Para el desarrollo de la plataforma, se adoptó la metodología de integración continua [74] que permitió ejecutar tests automáticos en un servidor y también desplegar aplicaciones en una página web. En cuanto a la metodología utilizada para la validación se adoptó un marco para la formulación de teorías en la ingeniería del software [181]. Esto requiere el desarrollo de modelos y proposiciones que han de ser validados dentro de un ámbito de investigación definido, y que sirvan para guiar al investigador en la búsqueda de la evidencia necesaria para justificarla. La validación del modelo fue desarrollada mediante una encuesta online en tres rondas con un número creciente de invitados. El cuestionario fue enviado a 134 contactos y distribuido en algunos canales públicos como listas de correo y redes sociales. El objetivo era evaluar la legibilidad del modelo, su nivel de cobertura del dominio y su potencial utilidad en el diseño de sistemas derivados. El cuestionario incluía preguntas cuantitativas de tipo Likert y campos para recolección de comentarios. La plataforma de desarrollo fue validada en dos etapas. En la primera etapa se utilizó la plataforma en un experimento a pequeña escala, que consistió en una sesión de entrenamiento de 12 horas en la que 4 desarrolladores tuvieron que desarrollar algunos casos de uso y reunirse en un grupo focal para discutir su uso. La segunda etapa se realizó durante los tests de un proyecto en gran escala llamado HeartCycle [160]. En este proyecto un equipo de diseñadores y programadores desarrollaron tres aplicaciones en el campo de las enfermedades cardio-vasculares. Una de estas aplicaciones fue testeada en un ensayo clínico con pacientes reales. Al analizar el proyecto, el equipo de desarrollo se reunió en un grupo focal para identificar las ventajas y desventajas de la plataforma y su utilidad. Resultados Por lo que concierne el modelo que describe el dominio del pHealth, la parte conceptual incluye una descripción de los roles principales y las preocupaciones de los participantes, un modelo de los artefactos TIC que se usan comúnmente y un modelo para representar los datos típicos que son necesarios formalizar e intercambiar entre sistemas basados en pHealth. El modelo funcional incluye un conjunto de 18 escenarios, repartidos en: punto de vista de la persona asistida, punto de vista del cuidador, punto de vista del desarrollador, punto de vista de los proveedores de tecnologías y punto de vista de las autoridades; y un conjunto de 52 casos de uso repartidos en 6 categorías: actividades de la persona asistida, reacciones del sistema, actividades del cuidador, \engagement" del usuario, actividades del desarrollador y actividades de despliegue. Como resultado del cuestionario de validación del modelo, un total de 65 personas revisó el modelo proporcionando su nivel de acuerdo con las dimensiones evaluadas y un total de 248 comentarios sobre cómo mejorar el modelo. Los conocimientos de los participantes variaban desde la ingeniería del software (70%) hasta las especialidades médicas (15%), con declarado interés en eHealth (24%), mHealth (16%), Ambient Assisted Living (21%), medicina personalizada (5%), sistemas basados en pHealth (15%), informática médica (10%) e ingeniería biomédica (8%) con una media de 7.25_4.99 años de experiencia en estas áreas. Los resultados de la encuesta muestran que los expertos contactados consideran el modelo fácil de leer (media de 1.89_0.79 siendo 1 el valor más favorable y 5 el peor), suficientemente abstracto (1.99_0.88) y formal (2.13_0.77), con una cobertura suficiente del dominio (2.26_0.95), útil para describir el dominio (2.02_0.7) y para generar sistemas más específicos (2_0.75). Los expertos también reportan un interés parcial en utilizar el modelo en su trabajo (2.48_0.91). Gracias a sus comentarios, el modelo fue mejorado y enriquecido con conceptos que faltaban, aunque no se pudo demonstrar su mejora en las dimensiones evaluadas, dada la composición diferente de personas en las tres rondas de evaluación. Desde el modelo, se generó una plataforma de desarrollo llamada \pHealth Patient Platform (pHPP)". La plataforma desarrollada incluye librerías, herramientas de programación y desarrollo, un tutorial y una aplicación de ejemplo. Se definieron cuatro módulos principales de la arquitectura: el Data Collection Engine, que permite abstraer las fuentes de datos como sensores o servicios externos, mapeando los datos a bases de datos u ontologías, y permitiendo interacción basada en eventos; el GUI Engine, que abstrae la interfaz de usuario en un modelo de interacción basado en mensajes; y el Rule Engine, que proporciona a los desarrolladores un medio simple para programar la lógica de la aplicación en forma de reglas \if-then". Después de que la plataforma pHPP fue utilizada durante 5 años en el proyecto HeartCycle, 5 desarrolladores fueron reunidos en un grupo de discusión para analizar y evaluar la plataforma. De estas evaluaciones se concluye que la plataforma fue diseñada para encajar las necesidades de los ingenieros que trabajan en la rama, permitiendo la separación de problemas entre las distintas especialidades, y simplificando algunas tareas de desarrollo como el manejo de datos y la interacción asíncrona. A pesar de ello, se encontraron algunos defectos a causa de la inmadurez de algunas tecnologías empleadas, y la ausencia de algunas herramientas específicas para el dominio como el procesado de datos o algunos protocolos de comunicación relacionados con la salud. Dentro del proyecto HeartCycle la plataforma fue utilizada para el desarrollo de la aplicación \Guided Exercise", un sistema TIC para la rehabilitación de pacientes que han sufrido un infarto del miocardio. El sistema fue testeado en un ensayo clínico randomizado en el cual a 55 pacientes se les dio el sistema para su uso por 21 semanas. De los resultados técnicos del ensayo se puede concluir que, a pesar de algunos errores menores prontamente corregidos durante el estudio, la plataforma es estable y fiable. Conclusiones La investigación llevada a cabo en esta Tesis y los resultados obtenidos proporcionan las respuestas a las tres preguntas de investigación que motivaron este trabajo: RQ1 Se ha desarrollado un modelo para representar el dominio de los sistemas personalizados de salud. La evaluación hecha por los expertos de la rama concluye que el modelo representa el dominio con precisión y con un balance apropiado entre abstracción y detalle. RQ2 Se ha desarrollado, con éxito, una plataforma de desarrollo basada en el modelo. RQ3 Se ha demostrado que la plataforma es capaz de ayudar a los desarrolladores en la creación de software pHealth complejos. Las ventajas de la plataforma han sido demostradas en el ámbito de un proyecto de gran escala, aunque el enfoque genérico adoptado indica que la plataforma podría ofrecer beneficios también en otros contextos. Los resultados de estas evaluaciones ofrecen indicios de que, ambos, el modelo y la plataforma serán buenos candidatos para poderse convertir en una referencia para futuros desarrollos de sistemas pHealth. ABSTRACT Background Europe is living in an unsustainable situation. The economic crisis has been reducing governments' economic resources since 2008 and threatening social and health systems, while the proportion of older people in the European population continues to increase so that it is foreseen that in 2050 there will be only two workers per retiree [54]. To this situation it should be added the rise, strongly related to age, of chronic diseases the burden of which has been estimated to be up to the 7% of a country's gross domestic product [51]. There is a need for a paradigm shift, the need for a new way of caring for people's health, shifting the focus from curing conditions that have arisen to a sustainable and effective approach with the emphasis on prevention. Some advocate the adoption of personalised health care (pHealth), a model where medical practices are tailored to the patient's unique life, from the detection of risk factors to the customization of treatments based on each individual's response [81]. Personalised health is often associated to the use of Information and Communications Technology (ICT), that, with its exponential development, offers interesting opportunities for improving healthcare. The shift towards pHealth is slowly taking place, both in research and in industry, but the change is not significant yet. Many barriers still exist related to economy, politics and culture, while others are purely technological, like the lack of interoperable information systems [199]. Though interoperability aspects are evolving, there is still the need of a reference design, especially tackling implementation and large scale deployment of pHealth systems. This thesis contributes to organizing the subject of ICT systems for personalised health into a reference model that allows for the creation of software development platforms to ease common development issues in the domain. Research questions RQ1 Is it possible to define a model, based on software engineering techniques, for representing the personalised health domain in an abstract and representative way? RQ2 Is it possible to build a development platform based on this model? RQ3 Does the development platform help developers create complex integrated pHealth systems? Methods As method for describing the model, the ISO/IEC/IEEE 42010 framework [25] is adopted for its generality and high level of abstraction. The model is specified in different parts: a conceptual model, which makes use of concept maps, for representing stakeholders, artefacts and shared information, and in scenarios and use cases for the representation of the functionalities of pHealth systems. The model was derived from literature analysis, including 7 industrial and scientific reports, 9 electronic standards, 10 conference proceedings papers, 37 journal papers, 25 websites and 5 books. Based on the reference model, requirements were drawn for building the development platform enriched with a set of requirements gathered in a survey run among 11 experienced engineers. For developing the platform, the continuous integration methodology [74] was adopted which allowed to perform automatic tests on a server and also to deploy packaged releases on a web site. As a validation methodology, a theory building framework for SW engineering was adopted from [181]. The framework, chosen as a guide to find evidence for justifying the research questions, imposed the creation of theories based on models and propositions to be validated within a scope. The validation of the model was conducted as an on-line survey in three validation rounds, encompassing a growing number of participants. The survey was submitted to 134 experts of the field and on some public channels like relevant mailing lists and social networks. Its objective was to assess the model's readability, its level of coverage of the domain and its potential usefulness in the design of actual, derived systems. The questionnaires included quantitative Likert scale questions and free text inputs for comments. The development platform was validated in two scopes. As a small-scale experiment, the platform was used in a 12 hours training session where 4 developers had to perform an exercise consisting in developing a set of typical pHealth use cases At the end of the session, a focus group was held to identify benefits and drawbacks of the platform. The second validation was held as a test-case study in a large scale research project called HeartCycle the aim of which was to develop a closed-loop disease management system for heart failure and coronary heart disease patients [160]. During this project three applications were developed by a team of programmers and designers. One of these applications was tested in a clinical trial with actual patients. At the end of the project, the team was interviewed in a focus group to assess the role the platform had within the project. Results For what regards the model that describes the pHealth domain, its conceptual part includes a description of the main roles and concerns of pHealth stakeholders, a model of the ICT artefacts that are commonly adopted and a model representing the typical data that need to be formalized among pHealth systems. The functional model includes a set of 18 scenarios, divided into assisted person's view, caregiver's view, developer's view, technology and services providers' view and authority's view, and a set of 52 Use Cases grouped in 6 categories: assisted person's activities, system reactions, caregiver's activities, user engagement, developer's activities and deployer's activities. For what concerns the validation of the model, a total of 65 people participated in the online survey providing their level of agreement in all the assessed dimensions and a total of 248 comments on how to improve and complete the model. Participants' background spanned from engineering and software development (70%) to medical specialities (15%), with declared interest in the fields of eHealth (24%), mHealth (16%), Ambient Assisted Living (21%), Personalized Medicine (5%), Personal Health Systems (15%), Medical Informatics (10%) and Biomedical Engineering (8%) with an average of 7.25_4.99 years of experience in these fields. From the analysis of the answers it is possible to observe that the contacted experts considered the model easily readable (average of 1.89_0.79 being 1 the most favourable scoring and 5 the worst), sufficiently abstract (1.99_0.88) and formal (2.13_0.77) for its purpose, with a sufficient coverage of the domain (2.26_0.95), useful for describing the domain (2.02_0.7) and for generating more specific systems (2_0.75) and they reported a partial interest in using the model in their job (2.48_0.91). Thanks to their comments, the model was improved and enriched with concepts that were missing at the beginning, nonetheless it was not possible to prove an improvement among the iterations, due to the diversity of the participants in the three rounds. From the model, a development platform for the pHealth domain was generated called pHealth Patient Platform (pHPP). The platform includes a set of libraries, programming and deployment tools, a tutorial and a sample application. The main four modules of the architecture are: the Data Collection Engine, which allows abstracting sources of information like sensors or external services, mapping data to databases and ontologies, and allowing event-based interaction and filtering, the GUI Engine, which abstracts the user interface in a message-like interaction model, the Workow Engine, which allows programming the application's user interaction ows with graphical workows, and the Rule Engine, which gives developers a simple means for programming the application's logic in the form of \if-then" rules. After the 5 years experience of HeartCycle, partially programmed with pHPP, 5 developers were joined in a focus group to discuss the advantages and drawbacks of the platform. The view that emerged from the training course and the focus group was that the platform is well-suited to the needs of the engineers working in the field, it allowed the separation of concerns among the different specialities and it simplified some common development tasks like data management and asynchronous interaction. Nevertheless, some deficiencies were pointed out in terms of a lack of maturity of some technological choices, and for the absence of some domain-specific tools, e.g. for data processing or for health-related communication protocols. Within HeartCycle, the platform was used to develop part of the Guided Exercise system, a composition of ICT tools for the physical rehabilitation of patients who suffered from myocardial infarction. The system developed using the platform was tested in a randomized controlled clinical trial, in which 55 patients used the system for 21 weeks. The technical results of this trial showed that the system was stable and reliable. Some minor bugs were detected, but these were promptly corrected using the platform. This shows that the platform, as well as facilitating the development task, can be successfully used to produce reliable software. Conclusions The research work carried out in developing this thesis provides responses to the three three research questions that were the motivation for the work. RQ1 A model was developed representing the domain of personalised health systems, and the assessment of experts in the field was that it represents the domain accurately, with an appropriate balance between abstraction and detail. RQ2 A development platform based on the model was successfully developed. RQ3 The platform has been shown to assist developers create complex pHealth software. This was demonstrated within the scope of one large-scale project, but the generic approach adopted provides indications that it would offer benefits more widely. The results of these evaluations provide indications that both the model and the platform are good candidates for being a reference for future pHealth developments.

Video-based tasks for emotional processing rehabilitation in schizophrenia

Schizophrenia is a mental disorder characterized by a breakdown of cognitive processes and by a deficit of typi-cal emotional responses. Effectiveness of computerized task has been demonstrated in the field of cognitive rehabilitation. However, current rehabilitation programs based on virtual environments normally focus on higher cognitive functions, not covering social cognition training. This paper presents a set of video-based tasks specifically designed for the rehabilita-tion of emotional processing deficits in patients in early stages of schizophrenia or schizoaffective disorders. These tasks are part of the Mental Health program of Guttmann NeuroPer-sonalTrainer® cognitive tele-rehabilitation platform, and entail innovation both from a clinical and technological per-spective in relation with former traditional therapeutic con-tents.

Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

A self-adaptive image processing application based on evolvable and scalable hardware

Evolvable Hardware (EH) is a technique that consists of using reconfigurable hardware devices whose configuration is controlled by an Evolutionary Algorithm (EA). Our system consists of a fully-FPGA implemented scalable EH platform, where the Reconfigurable processing Core (RC) can adaptively increase or decrease in size. Figure 1 shows the architecture of the proposed System-on-Programmable-Chip (SoPC), consisting of a MicroBlaze processor responsible of controlling the whole system operation, a Reconfiguration Engine (RE), and a Reconfigurable processing Core which is able to change its size in both height and width. This system is used to implement image filters, which are generated autonomously thanks to the evolutionary process. The system is complemented with a camera that enables the usage of the platform for real time applications.

A novel FPGA-based evolvable hardware system based on multiple processing arrays

In this paper, an architecture based on a scalable and flexible set of Evolvable Processing arrays is presented. FPGA-native Dynamic Partial Reconfiguration (DPR) is used for evolution, which is done intrinsically, letting the system to adapt autonomously to variable run-time conditions, including the presence of transient and permanent faults. The architecture supports different modes of operation, namely: independent, parallel, cascaded or bypass mode. These modes of operation can be used during evolution time or during normal operation. The evolvability of the architecture is combined with fault-tolerance techniques, to enhance the platform with self-healing features, making it suitable for applications which require both high adaptability and reliability. Experimental results show that such a system may benefit from accelerated evolution times, increased performance and improved dependability, mainly by increasing fault tolerance for transient and permanent faults, as well as providing some fault identification possibilities. The evolvable HW array shown is tailored for window-based image processing applications.