Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

QuiiQ automation foundation

Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

Attribute Equilibrium Dominance Reduction Accelerator (DCCAEDR) Based on Distributed Coevolutionary Cloud and Its Application in Medical Records

© 2013 IEEE. Aimed at the tremendous challenge of attribute reduction for big data mining and knowledge discovery, we propose a new attribute equilibrium dominance reduction accelerator (DCCAEDR) based on the distributed coevolutionary cloud model. First, the framework of N-populations distributed coevolutionary MapReduce model is designed to divide the entire population into N subpopulations, sharing the reward of different subpopulations' solutions under a MapReduce cloud mechanism. Because the adaptive balancing between exploration and exploitation can be achieved in a better way, the reduction performance is guaranteed to be the same as those using the whole independent data set. Second, a novel Nash equilibrium dominance strategy of elitists under the N bounded rationality regions is adopted to assist the subpopulations necessary to attain the stable status of Nash equilibrium dominance. This further enhances the accelerator's robustness against complex noise on big data. Third, the approximation parallelism mechanism based on MapReduce is constructed to implement rule reduction by accelerating the computation of attribute equivalence classes. Consequently, the entire attribute reduction set with the equilibrium dominance solution can be achieved. Extensive simulation results have been used to illustrate the effectiveness and robustness of the proposed DCCAEDR accelerator for attribute reduction on big data. Furthermore, the DCCAEDR is applied to solve attribute reduction for traditional Chinese medical records and to segment cortical surfaces of the neonatal brain 3-D-MRI records, and the DCCAEDR shows the superior competitive results, when compared with the representative algorithms

Large scale interoperability in the context of Future Internet

La croissance de l Internet en tant que plateforme d approvisionnement à grande échelled approvisionnement de contenus multimédia a été une grande success story du 21e siécle.Toutefois, les applications multimédia, avec les charactéristiques spécifiques de leur trafic ainsique les les exigences des nouveaux services, posent un défi intéressant en termes de découverte,de mobilité et de gestion. En outre, le récent élan de l Internet des objets a rendu très nécessairela revitalisation de la recherche pour intégrer des sources hétérogènes d information à travers desréseaux divers. Dans cet objectif, les contributions de cette thèse essayent de trouver un équilibreentre l hétérogénéité et l interopérabilité, pour découvrir et intégrer les sources hétérogènesd information dans le contexte de l Internet du Futur.La découverte de sources d information sur différents réseaux requiert une compréhensionapprofondie de la façon dont l information est structurée et quelles méthodes spécifiques sontutilisés pour communiquer. Ce processus a été régulé à l aide de protocoles de découverte.Cependant, les protocoles s appuient sur différentes techniques et sont conçues en prenant encompte l infrastructure réseau sous-jacente, limitant ainsi leur capacité à franchir la limite d unréseau donné. Pour résoudre ce problème, le première contribution dans cette thèse tente detrouver une solution équilibrée permettant aux protocoles de découverte d interagir les uns avecles autres, tout en fournissant les moyens nécessaires pour franchir les frontières entre réseaux.Dans cet objectif, nous proposons ZigZag, un middleware pour réutiliser et étendre les protocolesde découverte courants, conçus pour des réseaux locaux, afin de découvrir des servicesdisponibles dans le large. Notre approche est basée sur la conversion de protocole permettant ladécouverte de service indépendamment de leur protocole de découverte sous-jacent. Toutefois,dans les réaux de grande échelle orientée consommateur, la quantité des messages de découvertepourrait rendre le réseau inutilisable. Pour parer à cette éventualité, ZigZag utilise le conceptd agrégation au cours du processus de découverte. Grâce à l agrégation, ZigZag est capabled intégrer plusieurs réponses de différentes sources supportant différents protocoles de découverte.En outre, la personnalisation du processus d agrégation afin de s aligner sur ses besoins,requiert une compréhension approfondie des fondamentaux de ZigZag. À cette fin, nous proposonsune seconde contribution: un langage flexible pour aider à définir les politiques d unemanière propre et efficace.The growth of the Internet as a large scale media provisioning platform has been a great successstory of the 21st century. However, multimedia applications, with their specific traffic characteristicsand novel service requirements, pose an interesting challenge in terms of discovery,mobility and management. Furthermore, the recent impetus to Internet of things has made it verynecessary, to revitalize research in order to integrate heterogeneous information sources acrossnetworks. Towards this objective, the contributions in this thesis, try to find a balance betweenheterogeneity and interoperability, to discovery and integrate heterogeneous information sourcesin the context of Future Internet.Discovering information sources across networks need a through understanding of how theinformation is structured and what specific methods they follow to communicate. This processhas been regulated with the help of discovery protocols. However, protocols rely on differenttechniques and are designed taking the underlying network infrastructure into account. Thus,limiting the capability of some protocols to cross network boundary. To address this issue, thefirst contribution in this thesis tries to find a balanced solution to enable discovery protocols tointeroperate with each other as well as provide the necessary means to cross network boundaries.Towards this objective, we propose ZigZag, a middleware to reuse and extend current discoveryprotocols, designed for local networks, to discover available services in the large. Our approachis based on protocol translation to enable service discovery irrespectively of their underlyingdiscovery protocol. Although, our approach provides a step forward towards interoperability inthe large. We needed to make sure that discovery messages do not create a bottleneck for thenetwork.In large scale consumer oriented network, service discovery messages could render the networkunusable. To counter this, ZigZag uses the concept of aggregation during the discoveryprocess. Using aggregation ZigZag is able to integrate several replies from different servicesources supporting different discovery protocols. However, to customize the aggregation processto suit once needs, requires a through understanding of ZigZag fundamentals. To this end, wepropose our second contribution, a flexible policy language that can help define policies in aclean and effective way. In addition, the policy language has some added advantages in terms ofdynamic management. It provides features like delegation, runtime time policy management andlogging. We tested our approach with the help of simulations, the results showed that ZigZag canboth reduce the number of messages that flow through the network, and provide value sensitiveinformation to the requesting entity.Although, ZigZag is designed to discover media services in the large. It can very well be usedin other domains like home automation and smart spaces. While, the flexible pluggable modulardesign of the policy language enables it to be used in other applications like for instance, e-mail.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

A Dynamic User-Centric Mobile Context Model

Context-aware systems can dynamically adapt to user situations to provide smarter services. In general, context refers to the information that can be used to characterize these situations, and context models are deployed to specify contextual information described in context-aware systems. However, even though user context is highly dynamic, existing context models either focus on modeling static views of context or lack appropriate design abstractions to deal with dynamic aspects and interactions involving contextual elements such location, time, user roles, social relationships, and changing preferences. Moreover, virtual environments have not been modelled by most of the existing context models even though online interaction is very common and popular. This thesis presents a dynamic user-centric context model that can be used to model the aspects of context-aware systems that are subject to frequent change. Four case studies are proposed to illustrate the applicability of the approach taken by this thesis, and they are in the domains of mobile e-healthcare, mobile commerce, mobile tourism, and mobile augmented reality gaming. Benefits of the proposed model include avoiding the development of context-aware systems from scratch, enabling future use of model-driven approaches, and reducing implementation effort

Extração de conhecimento a partir de fontes semi-estruturadas

The increasing number of small, cheap devices, full of sensing capabilities lead to an untapped source of data that can be explored to improve and optimize multiple systems, from small-scale home automation to large-scale applications such as agriculture monitoring, traffic flow and industrial maintenance prediction. Yet, hand in hand with this growth, goes the increasing difficulty to collect, store and organize all these new data. The lack of standard context representation schemes is one of the main struggles in this area. Furthermore, conventional methods for extracting knowledge from data rely on standard representations or a priori relations. These a priori relations add latent information to the underlying model, in the form of context representation schemes, table relations, or even ontologies. Nonetheless, these relations are created and maintained by human users. While feasible for small-scale scenarios or specific areas, this becomes increasingly difficult to maintain when considering the potential dimension of IoT and M2M scenarios. This thesis addresses the problem of storing and organizing context information from IoT/M2M scenarios in a meaningful way, without imposing a representation scheme or requiring a priori relations. This work proposes a d-dimension organization model, which was optimized for IoT/M2M data. The model relies on machine learning features to identify similar context sources. These features are then used to learn relations between data sources automatically, providing the foundations for automatic knowledge extraction, where machine learning, or even conventional methods, can rely upon to extract knowledge on a potentially relevant dataset. During this work, two different machine learning techniques were tackled: semantic and stream similarity. Semantic similarity estimates the similarity between concepts (in textual form). This thesis proposes an unsupervised learning method for semantic features based on distributional profiles, without requiring any specific corpus. This allows the organizational model to organize data based on concept similarity instead of string matching. Another advantage is that the learning method does not require input from users, making it ideal for massive IoT/M2M scenarios. Stream similarity metrics estimate the similarity between two streams of data. Although these methods have been extensively researched for DNA sequencing, they commonly rely on variants of the longest common sub-sequence. This PhD proposes a generative model for stream characterization, specially optimized for IoT/M2M data. The model can be used to generate statistically significant data’s streams and estimate the similarity between streams. This is then used by the context organization model to identify context sources with similar stream patterns. The work proposed in this thesis was extensively discussed, developed and published in several international publications. The multiple contributions in projects and collaborations with fellow colleagues, where parts of the work developed were used successfully, support the claim that although the context organization model (and subsequent similarity features) were optimized for IoT/M2M data, they can potentially be extended to deal with any kind of context information in a wide array of applications.O número crescente de dispositivos pequenos e baratos, repletos de capacidades sensoriais, criou uma nova fonte de dados que pode ser explorada para melhorar e otimizar vários sistemas, desde domótica em ambientes residenciais até aplicações de larga escala como monitorização agrícola, gestão de tráfego e manutenção preditiva a nível industrial. No entanto, este crescimento encontra-se emparelhado com a crescente dificuldade em recolher, armazenar e organizar todos estes dados. A inexistência de um esquema de representação padrão é uma das principais dificuldades nesta área. Além disso, métodos de extração de conhecimento convencionais dependem de representações padrão ou relações definidas a priori. No entanto estas relações são definidas e mantidas por utilizadores humanos. Embora seja viável para cenários de pequena escala ou áreas especificas, este tipo de relações torna-se cada vez mais difícil de manter quando se consideram cenários com a dimensão associado a IoT e M2M. Esta tese de doutoramento endereça o problema de armazenar e organizar informação de contexto de cenários de IoT/M2M, sem impor um esquema de representação ou relações a priori. Este trabalho propõe um modelo de organização com d dimensões, especialmente otimizado para dados de IoT/M2M. O modelo depende de características de machine learning para identificar fontes de contexto similares. Estas caracteristicas são utilizadas para aprender relações entre as fontes de dados automaticamente, criando as fundações para a extração de conhecimento automática. Quer machine learning quer métodos convencionais podem depois utilizar estas relações automáticas para extrair conhecimento em datasets potencialmente relevantes. Durante este trabalho, duas técnicas foram desenvolvidas: similaridade semântica e similaridade entre séries temporais. Similaridade semântica estima a similaridade entre conceitos (em forma textual). Este trabalho propõe um método de aprendizagem não supervisionado para features semânticas baseadas em perfis distributivos, sem exigir nenhum corpus específico. Isto permite ao modelo de organização organizar dados baseado em conceitos e não em similaridade de caracteres. Numa outra vantagem importante para os cenários de IoT/M2M, o método de aprendizagem não necessita de dados de entrada adicionados por utilizadores. A similaridade entre séries temporais são métricas que permitem estimar a similaridade entre várias series temporais. Embora estes métodos tenham sido extensivamente desenvolvidos para sequenciação de ADN, normalmente dependem de variantes de métodos baseados na maior sub-sequencia comum. Esta tese de doutoramento propõe um modelo generativo para caracterizar séries temporais, especialmente desenhado para dados IoT/M2M. Este modelo pode ser usado para gerar séries temporais estatisticamente corretas e estimar a similaridade entre múltiplas séries temporais. Posteriormente o modelo de organização identifica fontes de contexto com padrões temporais semelhantes. O trabalho proposto foi extensivamente discutido, desenvolvido e publicado em diversas publicações internacionais. As múltiplas contribuições em projetos e colaborações com colegas, onde partes trabalho desenvolvido foram utilizadas com sucesso, permitem reivindicar que embora o modelo (e subsequentes técnicas) tenha sido otimizado para dados IoT/M2M, podendo ser estendido para lidar com outros tipos de informação de contexto noutras áreas.The present study was developed in the scope of the Smart Green Homes Project [POCI-01-0247-FEDER-007678], a co-promotion between Bosch Termotecnologia S.A. and the University of Aveiro. It is financed by Portugal 2020 under the Competitiveness and Internationalization Operational Program, and by the European Regional Development Fund.Programa Doutoral em Informátic

Towards formalisation of situation-specific computations in pervasive computing environments

We have categorised the characteristics and the content of pervasive computing environments (PCEs), and demonstrated why a non-dynamic approach to knowledge conceptualisation in PCEs does not fulfil the expectations we may have from them. Consequently, we have proposed a formalised computational model, the FCM, for knowledge representation and reasoning in PCEs which, secures the delivery of situation and domain specific services to their users. The proposed model is a user centric model, materialised as a software engineering solution, which uses the computations generated from the FCM, stores them within software architectural components, which in turn can be deployed using modern software technologies. The model has also been inspired by the Semantic Web (SW) vision and provision of SW technologies. Therefore, the FCM creates a semantically rich situation-specific PCE based on SWRL-enabled OWL ontologies that allows reasoning about the situation in a PCE and delivers situation specific service. The proposed FCM model has been illustrated through the example of remote patient monitoring in the healthcare domain. Numerous software applications generated from the FCM have been deployed using Integrated Development Environments and OWL-API