Un sistema de gestión de sequías apropiado requiere de la anticipación de los posibles efectos que un episodio de este tipo tenga sobre el sistema de recursos hídricos. Esta tarea sin embargo resulta más complicada de lo que parece. En primer lugar, debido al alto grado de incertidumbre existente en la predicción de variables hidrológicas futuras. Y en segundo, debido al riesgo de sobrerreacción en la activación de medidas de mitigación generando falsa sensación de escasez, o sequía artificial. A este respecto, los planes especiales de sequía proveen de herramientas para la gestión eficiente de situaciones con escasez de recursos y la preparación de cara a futuros eventos. De todos modos, las diferentes estrategias de operación seguidas en cada sistema de recursos hídricos hacen que las herramientas que en algunos casos resultaron altamente útiles no lo sean tanto cuando se aplican en sistemas distintos. Debido a la falta de tiempo y/o al exceso de confianza en los trabajos realizados por terceros, con excelentes resultados en sus respectivos casos, a veces se cae en el error de implementar metodologías no del todo apropiadas en sistemas con requisitos completamente distintos. El desarrollo y utilización de metodologías generalizadas aplicables a diferentes sistemas y capaces de proporcionar resultados adaptados a cada caso es, por tanto, muy deseable. Este es el caso de las herramientas de modelación de sistemas de recursos hídricos generalizadas. Estas permiten homogeneizar los procesos mientras siguen siendo los suficientemente adaptables para proporcionar resultados apropiados para cada caso de estudio. Esta tesis presenta una serie de herramientas destinadas a avanzar en el análisis y comprensión de los sistemas de recursos hídricos, haciendo énfasis en la prevención de sequías y la gestión de riesgos. Las herramientas desarrolladas incluyen: un modelo de optimización generalizado para esquemas de recursos hídricos, con capacidad para la representación detallada de cualquier sistema de recursos hídricos, y una metodología de análisis de riesgo basada en la optimización de Monte Carlo con múltiples series sintéticas. Con estas herramientas es posible incluir tanto la componente superficial como la subterránea del sistema estudiado dentro del proceso de optimización. La optimización está basada en la resolución iterativa de redes de flujo. Se probó la consistencia y eficiencia de diferentes algoritmos de resolución para encontrar un balance entre la velocidad de cálculo, el número de iteraciones, y la consistencia de los resultados, aportando recomendaciones para el uso de cada algoritmo dadas las diferencias entre los mismos. Las herramientas desarrolladas se aplican en dos casos de estudio reales en la evaluación y posibilidad de complementación de los sistemas de monitorización y alerta temprana de sequías existentes en los mismos. En el primer caso, se propone un enfoque alternativo para la monitorización de la sequía en el sistema de operación anual del río Órbigo (España), complementándolo con la utilización de la metodología de análisis de riesgo. En el segundo caso, las herramientas se emplean en un sistema con una estrategia de operación completamente distinta. Se estudia como el análisis de riesgo de la gestión óptima puede ayudar a la activación anticipada de los escenarios de sequía en los sistemas de los ríos Júcar y Turia, cuya operación es hiperanual. En esta ocasión, el sistema de indicadores existente goza de una gran confianza por parte de los usuarios. La metodología de análisis de riesgo es, sin embargo, capaz de anticipar los eventos de sequía con mayor alarma, aspecto que es deseable si se quiere evitar que los episodios en desarrollo vayan a más. En ambos casos se muestra como la evaluación anticipada de las posibles situaciones futuras del sistema permiten una definición confiable de los escenarios de sequía con suficiente antelación para la activación efectiva de medidas de prevención y/o mitigación en caso de ser necesarias. La utilización de indicadores provenientes de modelos frente a indicadores basados en datos observados es complementaria y ambos deberían utilizarse de forma conjunta para mejorar la gestión preventiva de los sistemas de recursos hídricos. El empleo de modelos de optimización en situaciones de incertidumbre hidrológica es muy apropiado gracias a la no necesidad de definir reglas de gestión para obtener los mejores resultados del sistema, y teniendo en cuenta que las reglas de operación habituales pueden no ser completamente adecuadas en estas ocasiones. / Haro Monteagudo, D. (2014). Methodology for the optimal management design of water resources system under hydrologic uncertainty [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/45996

Existing literature indicates that although both academics and practitioners recognize knowledge management (KM) as a source of competitive advantage, users are not always willing to use a knowledge management system (KMS). Because of the social nature of knowledge transfer, a KMS can be considered a socio-technical system. Many explanations have been presented for this failure to utilize the KMS. These explanations include a number of the socio-technical factors relating to people, processes, and technologies. While these factors may have significant explanatory power when examined independently, existing studies have not sufficiently addressed the interactions among all three socio-technical factors or their impacts on KMS usage. The goal of this study was to develop a comprehensive understanding of socio-technical factors that impact KMS usage within decision support systems (DSS). A comprehensive framework was presented that will be helpful in developing and improving KMS initiatives and thus improving KM across the organization. This study identified factors of people (self-efficacy, social ties, and ease of use), processes (leadership, culture/climate, and governance), and technologies (system & information quality, and technology fit) and their influence on KMS system usage. Analysis for this problem required a causal, non-contrived field study employing structural equation modeling. Founded on socio-technical systems theory, nine hypotheses were proposed. Data was collected using a 36 item survey distributed to KMS users from a variety of industries in the United States. Confirmatory factor analysis and an eight-stage structural equation modeling procedure were used to analyze 97 usable responses. The results confirmed that technology-oriented factors predicted knowledge seeking and contributing in DSS. Furthermore, significant positive relationships were confirmed between certain sociotechnical factors including: (1) people and process, (2) people and technology, (3) processes and technology, (4) processes and people, (5) technology and people, and (6) technology and processes. These findings extend the relevance and statistical power of existing studies on KMS usage. This study indicated that the most important concerns for increasing KMS usage were system quality, information quality, and technology fit. Results also confirmed that in the context of this study, people-oriented factors (self-efficacy, social ties, and ease of use/usefulness) and organizational process factors (leadership, organizational culture/climate, and governance) were not critical factors directly responsible for increasing KMS usage. However, the relationships among socio-technical factors all had positive significant relationships. Therefore, investments in people and process-oriented factors will create a more favorable perspective on technology-oriented factors, which in turn can increase KMS usage. On a practical front, this study provided indicators to managers regarding a number of desirable and undesirable conditions that should be taken into consideration when developing or implementing knowledge management initiatives and the systems to support them. This study offered an original contribution to the existing bodies of knowledge on socio-technical factors and KMS usage behavior. The constructs presented in this study highlighted the significance of social and technical relationships in understanding knowledge seeking and contribution in a decision-driven organization.

This thesis presents a new integrated tool and decision support framework to approach complex problems resulting from the interaction of many multi-criteria issues. The framework is embedded in an integrated tool called OUTDO (Oxford University Tool for Decision Organisation). OUTDO integrates Multi-Criteria Decision Analysis (MCDA), decision rationale management with a modified Issue-Based Information Systems (IBIS) representation, and probabilistic forecasting to effectively capture the essential reasons why decisions are made and to dynamically re-use the rationale. In doing so, it allows exploration of how changes in external parameters affect complicated and uncertain decision making processes in the present and in the future. Once the decision maker constructs his or her own decision process, OUTDO checks if the decision process is consistent and coherent and looks for possible ways to improve it using three new semantic-based decision support approaches. For this reason, two ontologies (the Decision Ontology and the Energy Ontology) were integrated into OUTDO to provide it with these semantic capabilities. The Decision Ontology keeps a record of the decision rationale extracted from OUTDO and the Energy Ontology describes the energy generation domain, focusing on the water requirement in thermoelectric power plants. A case study, with the objective of recommending electricity generation and steam condensation technologies for ten different regions in the UK, is used to verify OUTDO’s features and reach conclusions about the overall work.

Whether it is based on current emissions data or future projections of further growth, the building sector currently represent the largest and singular most important contributor to greenhouse gas (GHG) emissions globally. This notion is also supported by the Intergovernmental Panel on Climate Change based on projection scenarios for 2030 that emissions from buildings will be responsible for about one-third of total global emissions. As such, improving the energy efficiency of buildings has become a top priority worldwide. A significant majority of buildings that exist now will still exist in 2030 and beyond; therefore the greatest energy savings and carbon footprint reductions can be made through retrofit of existing buildings. A wide range of retrofit options are readily available, but methods to identify optimal solutions for a particular abatement project still constitute a major technical challenge. Investments in building energy retrofit technologies usually involve decision-making processes targeted at reducing operational energy consumption and maintenance bills. For this reason, retrofit decisions by building stakeholders are typically driven by financial considerations. However, recent trends towards environmentally conscious and resource-efficient design and retrofit have focused on the environmental merits of these options, emphasising a lifecycle approach to emissions reduction. Retrofit options available for energy savings have different performance characteristics and building stakeholders are required to establish an optimal solution, where competing objectives such as financial costs, energy consumption and environmental performance are taken into account. These key performance parameters cannot be easily quantified and compared by building stakeholders since they lack the resources to perform an effective decision analysis. In part, this is due to the inadequacy of existing methods to assess and compare performance indicators. Current methods to quantify these parameters are considered in isolation when making decisions about energy conservation in buildings. To effectively manage the reduction of lifecycle environmental impacts, it is necessary to link financial cost with both operational and embodied emissions. This thesis presents a novel deterministic decision support system (DSS) for the evaluation of economically and environmentally optimal retrofit of non-domestic buildings. The DSS integrates the key variables of economic and net environmental benefits to produce optimal decisions. These variables are used within an optimisation scheme that consists of integrated modules for data input, sensitivity analysis and takes into account the use of a set of retrofit options that satisfies a range of criteria (environmental, demand, cost and resource constraints); hierarchical course of action; and the evaluations of ‘best’ case scenario based on marginal abatement cost methods and Pareto optimisation. The steps involved in the system development are presented and its usefulness is evaluated using case study applications. The results of the applications are analysed and presented, verifying the feasibility of the DSS, whilst encouraging further improvements and extensions. The usefulness of the DSS as a tool for policy formulation and developments that can trigger innovations in retrofit product development processes and sustainable business models are also discussed. The methodology developed provides stakeholders with an efficient and reliable decision process that is informed by both environmental and financial considerations. Overall, the development of the DSS which takes a whole-life CO2 emission accounting framework and an economic assessment view-point, successfully demonstrates how value is delivered across different parts of the techno-economic system, especially as it pertains to financial gains, embodied and operational emissions reduction potential.

There is considerable opportunity to develop new modelling techniques within a Geographic Information Systems (GIS) framework for the development of sustainable marine cage culture. However, the spatial data sets are often uncertain and incomplete, therefore new spatial models employing “soft computing” methods such as fuzzy logic may be more suitable. The aim of this study is to develop a model using Neuro-fuzzy techniques in a 3D GIS (Arc View 3.2) to predict coastal environmental vulnerability for Atlantic salmon cage aquaculture. A 3D hydrodynamic model (3DMOHID) coupled to a particle-tracking model is applied to study the circulation patterns, dispersion processes and residence time in Mulroy Bay, Co. Donegal Ireland, an Irish fjard (shallow fjordic system), an area of restricted exchange, geometrically complicated with important aquaculture activities. The hydrodynamic model was calibrated and validated by comparison with sea surface and water flow measurements. The model provided spatial and temporal information on circulation, renewal time, helping to determine the influence of winds on circulation patterns and in particular the assessment of the hydrographic conditions with a strong influence on the management of fish cage culture. The particle-tracking model was used to study the transport and flushing processes. Instantaneous massive releases of particles from key boxes are modelled to analyse the ocean-fjord exchange characteristics and, by emulating discharge from finfish cages, to show the behaviour of waste in terms of water circulation and water exchange. In this study the results from the hydrodynamic model have been incorporated into GIS to provide an easy-to-use graphical user interface for 2D (maps), 3D and temporal visualization (animations), for interrogation of results. v Data on the physical environment and aquaculture suitability were derived from a 3- dimensional hydrodynamic model and GIS for incorporation into the final model framework and included mean and maximum current velocities, current flow quiescence time, water column stratification, sediment granulometry, particulate waste dispersion distance, oxygen depletion, water depth, coastal protection zones, and slope. The Neuro-fuzzy classification model NEFCLASS–J, was used to develop learning algorithms to create the structure (rule base) and the parameters (fuzzy sets) of a fuzzy classifier from a set of classified training data. A total of 42 training sites were sampled using stratified random sampling from the GIS raster data layers, and the vulnerability categories for each were manually classified into four categories based on the opinions of experts with field experience and specific knowledge of the environmental problems investigated. The final products, GIS/based Neuro Fuzzy maps were achieved by combining modeled and real environmental parameters relevant to marine fin fish Aquaculture. Environmental vulnerability models, based on Neuro-fuzzy techniques, showed sensitivity to the membership shapes of the fuzzy sets, the nature of the weightings applied to the model rules, and validation techniques used during the learning and validation process. The accuracy of the final classifier selected was R=85.71%, (estimated error value of ±16.5% from Cross Validation, N=10) with a Kappa coefficient of agreement of 81%. Unclassified cells in the whole spatial domain (of 1623 GIS cells) ranged from 0% to 24.18 %. A statistical comparison between vulnerability scores and a significant product of aquaculture waste (nitrogen concentrations in sediment under the salmon cages) showed that the final model gave a good correlation between predicted environmental vi vulnerability and sediment nitrogen levels, highlighting a number of areas with variable sensitivity to aquaculture. Further evaluation and analysis of the quality of the classification was achieved and the applicability of separability indexes was also studied. The inter-class separability estimations were performed on two different training data sets to assess the difficulty of the class separation problem under investigation. The Neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability has demonstrated an ability to derive an accurate and reliable classification into areas of different levels of environmental vulnerability using a minimal number of training sets. The output will be an environmental spatial model for application in coastal areas intended to facilitate policy decision and to allow input into wider ranging spatial modelling projects, such as coastal zone management systems and effective environmental management of fish cage aquaculture.

Approved for public release, distribution is unlimited / The Navy has to choose the number of, and designs for, ships in the Combat Logistics Force (CLF), and then plan how to use them to provide logistical support to our Carrier Strike Groups, Expeditionary Strike Groups, and Seabasing platforms engaged in any variety of worldwide conflicts. CLF ships are very expensive to build and equip and our budget is limited --- we need to make sure the ships we buy and the way we integrate these with our CLF fleet can continue to provide the flexible support our Navy requires. We introduce a decision support tool using a global sea route and resupply base model, and a daily time resolution optimization of CLF ship activities to support any complete, worldwide scenario. Our result is an optimal, face-valid daily operational logistics plan - a schedule of evolutions for each available CLF ship. We discover exactly how to use CLF ships to support a notional, but particularly relevant, preemptive combat scenario with follow-on humanitarian assistance missions. Finally, we study how changing CLF ship numbers and missions can enhance operational effectiveness. / Lieutenant Commander, United States Navy

The growth in the interdependence and complexity of socio-technical systems requires the development of tools and techniques to aid in the formulation of better policies. The efforts of this research focus towards developing methodologies and support tools for better policy design and formulation. In this thesis, a new framework and a systematic approach for the formulation of policies are proposed. Focus has been directed to the interactions between policy measures, inspired by concepts in process design and network analysis. Furthermore, we have developed an agent-based approach to create a virtual environment for the exploration and analysis of different configurations of policy measures in order to build policy packages and test the effects of changes and uncertainties while formulating policies. By developing systematic approaches for the formulation and analysis of policies it is possible to analyse different configuration alternatives in greater depth, examine more alternatives and decrease the time required for the overall analysis. Moreover, it is possible to provide real-time assessment and feedback to the domain experts on the effect of changes in the configurations. These efforts ultimately help in forming more effective policies with synergistic and reinforcing attributes while avoiding internal contradictions. This research constitutes the first step towards the development of a general family of computer-based systems that support the design of policies. The results from this research also demonstrate the usefulness of computational approaches in addressing the complexity inherent in the formulation of policies. As a proof of concept, the proposed framework and methodologies have been applied to the formulation of policies that deal with transportation issues and emission reduction, but can be extended to other domains.

Introduction : Établir un diagnostic médical fiable requiert l’identification de la maladie d’un patient sur la base de l’observation de ses signes et symptômes. Par ailleurs, les ontologies constituent un formalisme adéquat et performant de représentation des connaissances biomédicales. Cependant, les ontologies classiques ne permettent pas de représenter les connaissances liées au processus du diagnostic médical : connaissances probabilistes et connaissances imprécises et vagues. Matériel et méthodes : Nous proposons des méthodes générales de représentation des connaissances afin de construire des ontologies adaptées au diagnostic médical. Ces méthodes permettent de représenter : (a) Les connaissances imprécises et vagues par la discrétisation des concepts (définition de plusieurs catégories distinctes à l’aide de valeurs seuils ou en représentant les différentes modalités possibles). (b) Les connaissances probabilistes (les sensibilités et les spécificités des signes pour les maladies, et les prévalences des maladies pour une population donnée) par la réification des relations ayant des arités supérieures à 2. (c) Les signes absents par des relations et (d) les connaissances liées au processus du diagnostic médical par des règles SWRL. Un moteur d’inférences abductif et probabiliste a été conçu et développé. Ces méthodes ont été testées à l’aide de dossiers patients réels. Résultats : Ces méthodes ont été appliquées à trois domaines (les maladies plasmocytaires, les urgences odontologiques et les lésions traumatiques du genou) pour lesquels des modèles ontologiques ont été élaborés. L’évaluation a permis de mesurer un taux moyen de 89,34% de résultats corrects. Discussion-Conclusion : Ces méthodes permettent d’avoir un modèle unique utilisable dans le cadre des raisonnements abductif et probabiliste, contrairement aux modèles proposés par : (a) Fenz qui n’intègre que le mode de raisonnement probabiliste et (b) García-crespo qui exprime les probabilités hors du modèle ontologique. L’utilisation d’un tel système nécessitera au préalable son intégration dans le système d’information hospitalier pour exploiter automatiquement les informations du dossier patient électronique. Cette intégration pourrait être facilitée par l’utilisation de l’ontologie du système. / Introduction: Making a reliable medical diagnosis requires the identification of the patient’s disease based on the observation of signs. Moreover, ontologies provide an adequate and efficient formalism for medical knowledge representation. However, classical ontologies do not allow representing knowledge associated with medical reasoning such as probabilistic, imprecise, or vague knowledge. Material and methods: In the current work, general knowledge representation methods are proposed. They aim at building ontologies fitting to medical diagnosis. They allow to represent: (a) imprecise or vague knowledge by discretizing concepts (definition of several distinct categories thanks to threshold values or by representing the various possible modalities), (b) probabilistic knowledge (sensitivity, specificity and prevalence) by reification of relations of arity greater than 2, (c) absent signs by relations and (d) medical reasoning and reasoning on the absent signs by SWRL rules. An abductive reasoning engine and a probabilistic reasoning engine were designed and implemented. The methods were evaluated by use of real patient records. Results: These methods were applied to three domains (the plasma cell diseases, the dental emergencies and traumatic knee injuries) for which the ontological models were developed. The average rate of correct diagnosis was 89.34 %. Discussion-Conclusion: In contrast with other methods proposed by Fenz and García-crespo, the proposed methods allow to have a unique model which can be used both for abductive and probabilistic reasoning. The use of such a system will require beforehand its integration in the hospital information system for the automatic exploitation of the electronic patient record. This integration might be made easier by the use of the ontology on which the system is based.

Dissertation thesis deals with development and implementation of Business Intelligence (BI) solutions for Small and Medium Sized Enterprises (SME) in the Czech Republic. This thesis represents climax of author's up to now effort that has been put into completing a methodological model for development of this kind of applications for SMEs using self-owned skills and minimum of external resources and costs. This thesis can be divided into five major parts. First part that describes used technologies is divided into two chapters. First chapter describes contemporary state of Business Intelligence concept and it also contains original taxonomy of Business Intelligence solutions. Second chapter describes two Knowledge Discovery in Databases (KDD) techniques that were used for building those BI solutions that are introduced in case studies. Second part describes the area of Czech SMEs, which is an environment where the thesis was written and which it is meant to contribute to. This environment is represented by one chapter that defines the differences of SMEs against large corporations. Furthermore, there are author's reasons why he is personally focusing on this area explained. Third major part introduces the results of survey that was conducted among Czech SMEs with support of Department of Information Technologies of Faculty of Informatics and Statistics of University of Economics in Prague. This survey had three objectives. First one was to map the readiness of Czech SMEs for BI solutions development and deployment. Second was to determine major problems and consequent decisions of Czech SMEs that could be supported by BI solutions and the third objective was to determine top factors preventing SMEs from developing and deploying BI solutions. Fourth part of the thesis is also the core one. In two chapters there is the original Methodology for development and deployment of BI solutions by SMEs described as well as other methodologies that were studied. Original methodology is partly based on famous CRISP-DM methodology. Finally, last part describes particular company that has become a testing ground for author's theories and that supports his research. In further chapters it introduces case-studies of development and deployment of those BI solutions in this company, that were build using contemporary BI and KDD techniques with respect to original methodology. In that sense, these case-studies verified theoretical methodology in real use.

L'objectif de cette thèse est de concevoir une architecture de services Web pour la dé-termination automatique d’un système de capteurs personnalisé, embarqué sous forme de vêtement intelligent dédié au self-care, afin de permettre à tout utilisateur profane d'enregistrer lui-même son propre électrocardiogramme (ECG), à tout moment et n’importe où. Le défi principal réside dans l'orchestration intelligente et dynamique de services métiers en fonction du contexte, pour qu’ils fournissent à l'utilisateur une solution personnalisée optimale tout en maîtrisant la complexité inhérente à la dépendance au contexte des interactions homme-machine, à l'extraction des connaissances des signes vitaux spécifiques à un sujet, et à l'automatisation de la reconfiguration des services. Une solution à ce défi est de créer une intelligence ambiante qui étend la notion d'informatique ubiquitaire et est capable d’offrir à l’instar d’un expert du domaine, une assistance intelligente personnalisée à tout citoyen. Nous proposons une méthodologie de construction d'une architecture DM-SOA orientée-services, dirigée à la fois par les données et par des modèles, pour la production de services métiers intelligents et tenant compte du contexte. Cette architecture permet l’automatisation d’activités sophistiquées et personnalisées qu’un expert mettrait en œuvre pour le traitement d’un cas individuel, à partir de ses connaissances professionnelles. La solution proposée est basée sur une nouvelle approche de modélisation dynamique de processus métiers, et l’implémentation de services reconfigurables automatiquement. Elle consiste à mettre en œuvre un environnement intelligent fondé sur une ontologie de processus métiers des concepts du domaine et de son contexte, et sur une base de règles pour l'orchestration contextuelle des services. Pour valider le bien-fondé de notre approche, nous avons proposé une ontologie pour l’automatisation de processus d’aide à la décision et nous l’avons évaluée dans le domaine de la cardiologie, en l’appliquant au problème de la sélection la plus adéquate possible d’un système de positionnement d’électrodes, spécifique à chaque individu, et capable de fournir un signal ECG de contenu diagnostique similaire à celui d'un ECG standard à 12 dérivations. Pour répondre aux besoins en situation de self-care tout en préservant la qualité diagnostique des signaux enregistrés à la demande, nous proposons la réalisation d’un nouveau système prototype de capture ECG-3D à trois modalités. Ce prototype a été testé sur huit jeunes adultes volontaires sains (4 hommes et 4 femmes) présentant diverses caractéristiques morphologiques. L'intelligence ambiante est fournie par un ensemble de services de qualité professionnelle, prêts à l’emploi par des utilisateurs profanes. Ces services peuvent être accessibles au travail, n’importe où, via des moyens classiquement utilisés chaque jour, et fournissent une aide appropriée aux utilisateurs non-compétents. / The aim of the thesis is to design a web services architecture that shall support the automatic determination of a personalised sensor-system, which is embedded in smart garments, and which shall be used in self-care in order to allow a profane user to record himself a personal electrocardiogram (ECG), at anytime and anywhere. The main challenge consists in the intelligent and dynamic orchestration of context-aware business services that supply the user with an optimal personalised solution, while mastering the system’s complexity: context dependent user and system interactions, knowledge extraction from subject-specific vital signs, services reconfiguration automation. The solution to this challenge is to create an Ambient Intelligence which goes beyond Ubiquitous Computing and is capable to replace an expert by proposing an Intelligent Assistance to any citizen. We propose a methodology expressed in terms of Data and Model driven Service Oriented Architecture (DM-SOA), which provides a framework for the production of context-aware intelligent business services. This architecture supports the automation of sophisticated and personalised expert activities, which apply professional knowledge to process an individual case. The proposed solution is based on a new dynamic business process modelling approach and in its implementation via automatically reconfigurable services. It consists in setting-up an intelligent environment based on a business process ontology of context-aware concepts and on related context handling rules for services orchestration. A core-ontology has been designed to support the automation of expert activities related to decision-making. The soundness of the method and of the underlying decision-making techniques has been demonstrated in the cardiology domain for selecting the most adequate subject-specific sensor-system, characterised by its ability to yield for an ECG signal of similar diagnostic content than a standard 12-lead ECG. We propose the design of a new three modalities sensor-system prototype as a response to the need of sensor-systems used on demand in self-care situations and ensuring diagnostic quality signals recordings. This prototype has been tested on a set of healthy volunteers presenting various characteristics in age, sex and morphology. In this context, Ambient Intelligence is demonstrated as an ensemble of professional quality services ready to use by profane users. These services are accessible pervasively, trough the objects people work with / wear on / use each day, and provide an appropriate guidance to the non-competent users. Such an Ambient Intelligence approach strongly supports the vision of an Information Society which corresponds to the long-term goal of the EU Information Society Technologies Research Programme that aims to ease knowledge access. The future healthcare will benefit from this approach and will significantly improve patient care.