Semantic linking through spaces for cyber-physical-socio intelligence:a methodology

Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know few links and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space — the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment CP3SME. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multi-disciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the CP3SME including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyber-physical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The CP3SME aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing

Stream Processing Languages in the Big Data Era

International audienc

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources

Contribution to the Design of Digital Supply Chain Governance Concepts for Sustainable Development of Biodiesel

Biodiesel sowie Biokraftstoffe wurden in der letzten Zeit zunehmend in mehreren Vorträgen und Konferenzen diskutiert. Während viele der wissenschaftlichen Untersuchungen die Produktionsprobleme, wie Effizienz, Diversifizierung und Prozesstechnologie (1, 2, 3 generation) behandelten, wurde jedoch nur in einigen davon Biodiesel aus der logistischen Perspektive betrachtet. Trotzdem stellt Biodiesel ein Problem für die logistischen Prozesse dar, so wie Anlagenplanung, Transport und Tourenplanung verbunden mit der Qualitätskontrolle entlang der Kontinuität der Rohmaterialversorgung. In einigen Entwicklungsländern ist das Management der Biodiesel-Industrie in eine Zwangslage geraten. Obwohl Entwicklungsländer gemeinsam eine nationale Behörde für das Biodiesel-Geschäft eingeführt haben, ist die Institution nach wie vor in den frühen Stadien der Standardisierung sowie Qualitätskontrolle. Derzeit gibt es keine Agentur für die Supply Chain Führung, die für die Integration des Biodiesel-Geschäftes zwischen vorgelagerten und nachgelagerten Bereiche fähig ist. In dieser Arbeit stellt der Autor eine Prozesstechnik vor, um die die Biodiesel-Industrie zu bewerten. Diese Prozedur kombiniert Geschäftsmodell/-analyse (unter Nutzung der General Electric/Mc. Kinsey Matrix), Simulation, Konzeptentwurf und ein Prototyping-System. Die Studie leistet einen wissenschaftlichen Beitrag für die Planung einer digitalen Biodiesel Supply Chain und bildet einen Rahmen für die Führung eines solchen Systems vom vorgelagertem zum nachgelagerten Bereich. Der Forscher verwendet eine ganzheitliche Betrachtung, wo Biodiesel nicht als eine gesonderte Einheit gesehen wird, aufgrund der Tatsache, dass es den Konsumenten, abhängig von unterschiedlichen Aspekten, von einer langen Kette ankommt. Um ihre Geschäfte zu managen, haben viele Unternehmen Enterprise-Resource-Planning eingeführt, aber leider waren sie nicht in der Lage die gesamte Wertschöpfungskette damit zu erreichen. Digitalisierung ist bei der Integration des Informationssystems von allen Supply Chain Mitgliedern wünschenswert. Um solch eine Idee anzupassen sowie den gesamten Prozess zu überwachen, muss ein Kontrollturm gebaut werden. In Folge dessen könnte die beste standardisierte Qualität und Nachhaltigkeit erreicht werden. Der Autor schlägt ebenso ein Übergangskonzept in der Implementierungsebene vor, aufgrund der Tatsache, dass die Supply Chain Mitglieder in der Realität keine ähnlichen Informationssystem-Standards zur Verfügung haben. Das Ergebnis der Literatur-Studien, Simulationen, Prototypenentwicklungen, theoretischen Argumente und Konzeptentwürfen präsentiert eine Digitalisierungsmuster in der Supply Chain von Biodiesel für die nachhaltige Entwicklung.Recently, biodiesel and biofuel have increasingly been discussed in several papers and conferences. However, only a few have examined biodiesel from the logistics perspective, while most of the scientific investigations have addressed the production issues, e.g. efficiency, diversification and processing technology (1st, 2nd or 3rd generation). In spite of this, biodiesel poses a problem for logistic processes, such as facility planning, transport, and routing-scheduling associated with quality control along with continuity of feedstock supply. In some developing countries, the management of the biodiesel industry has also become a predicament. Even though developed countries have commonly established a national agency in charge of the biodiesel business, the institution is still in the early stages of standardisation and quality control. Currently, there is no agency concerned with supply chain governance that is capable of integrating the biodiesel business from upstream to downstream. In this thesis, the author presents a procedural technique to assess the biodiesel industry. This procedure combines business modelling/analysis (using General Electric/Mc. Kinsey Matrix), simulation, conceptual design and a prototyping system. The study provides scientific insight for planning a digital biodiesel supply chain and proposes a framework for governing such a system from upstream to downstream. The researcher employs a holistic approach, where biodiesel is not seen as a separate entity because it comes to the consumers through a long chain dependent on various aspects. Currently, a number of companies have implemented Enterprise-Resource-Planning to manage their businesses, but unfortunately, they have not been able to reach the entire value chain. Digitalisation is desirable when integrating the Information Systems of all supply chain members. A control tower must be built to accommodate such an idea and monitor the entire process. Then, the best standardised quality and sustainability can be achieved. The author also offers a transition concept in the implementation level, because, in reality, the members in the supply chain have no similar Informastion-System standard. The results from literature studies, simulations, prototyping, theoretical arguments, and conceptual design present a digitalisation pattern in the biodiesel supply chain for sustainable development

Orta Doğu Teknik Üniversitesi- Bilgi Toplumu Teknolojileri- Mükemmeliyet merkez projesi

TÜBİTAK EEEAG Proje01.02.200

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario

The role of semantic web technologies for IoT data in underpinning environmental science

The advent of Internet of Things (IoT) technology has the potential to generate a huge amount of heterogeneous data at different geographical locations and with various temporal resolutions in environmental science. In many other areas of IoT deployment, volume and velocity dominate, however in environmental science, the more general pattern is quite distinct and often variety dominates. There exists a large number of small, heterogeneous and potentially complex datasets and the key challenge is to understand the interdependencies between these disparate datasets representing different environmental facets. These characteristics pose several data challenges including data interpretation, interoperability and integration, to name but a few, and there is a pressing need to address these challenges. The author postulates that Semantic Web technologies and associated techniques have the potential to address the aforementioned data challenges and support environmental science. The main goal of this thesis is to examine the potential role of Semantic Web technologies in making sense of such complex and heterogeneous environmental data in all its complexity. The thesis explores the state-of-the-art in the use of such technologies in the context of environmental science. After an in-depth assessment of related work, the thesis further examined the characteristics of environmental data through semi-structured interviews with leading experts. Through this, three key research challenges emerge: discovering interdependencies between disparate datasets, geospatial data integration and reasoning, and data heterogeneity. In response to these challenges, an ontology was developed that semantically enriches all sensor measurements stemmed from an experimental Environmental IoT infrastructure. The resultant ontology was evaluated through three real-world use-cases derived from the interviews. This led to a number of major contributions from this work including: the development of an ontology tailored for streaming environmental data offering semantic enrichment of IoT data, support for spatio-temporal data integration and reasoning, and the analysis of unique characteristics of environmental science around data

SeMoM: a semantic middleware for IoT healthcare applications

De nos jours, l'internet des objets (IoT) connaît un intérêt considérable tant de la part du milieu universitaire que de l'industrie. Il a contribué à améliorer la qualité de vie, la croissance des entreprises et l'efficacité dans de multiples domaines. Cependant, l'hétérogénéité des objets qui peuvent être connectés dans de tels environnements, rend difficile leur interopérabilité. En outre, les observations produites par ces objets sont générées avec différents vocabulaires et formats de données. Cette hétérogénéité de technologies dans le monde IoT rend nécessaire l'adoption de solutions génériques à l'échelle mondiale. De plus, elle rend difficile le partage et la réutilisation des données dans d'autres buts que ceux pour lesquels elles ont été initialement mises en place. Dans cette thèse, nous abordons ces défis dans le contexte des applications de santé. Pour cela, nous proposons de transformer les données brutes issues de capteurs en connaissances et en informations en s'appuyant sur les ontologies. Ces connaissances vont être partagées entre les différents composants du système IoT. En ce qui concerne les défis d'hétérogénéité et d'interopérabilité, notre contribution principale est une architecture IoT utilisant des ontologies pour permettre le déploiement d'applications IoT sémantiques. Cette approche permet de partager les observations des capteurs, la contextualisation des données et la réutilisation des connaissances et des informations traitées. Les contributions spécifiques comprennent : * Conception d'une ontologie " Cognitive Semantic Sensor Network ontology (CoSSN) " : Cette ontologie vise à surmonter les défis d'interopérabilité sémantiques introduits par la variété des capteurs potentiellement utilisés. CoSSN permet aussi de modéliser la représentation des connaissances des experts. * Conception et mise en œuvre de SeMoM: SeMoM est une architecture flexible pour l'IoT intégrant l'ontologie CoSSN. Elle s'appuie sur un middleware orienté message (MoM) pour offrir une solution à couplage faible entre les composants du système. Ceux-ci peuvent échanger des données d'observation sémantiques de manière flexible à l'aide du paradigme producteur/consommateur. Du point de vue applicatif, nous sommes intéressés aux applications de santé. Dans ce domaine, les approches spécifiques et les prototypes individuels sont des solutions prédominantes ce qui rend difficile la collaboration entre différentes applications, en particulier dans un cas de patients multi-pathologies. En ce qui concerne ces défis, nous nous sommes intéressés à deux études de cas: 1) la détection du risque de développement des escarres chez les personnes âgées et 2) la détection des activités de la vie quotidienne (ADL) de personnes pour le suivi et l'assistance à domicile : * Nous avons développé des extensions de CoSSN pour décrire chaque concept en lien avec les deux cas d'utilisation. Nous avons également développé des applications spécifiques grâce à SeMoM qui mettent en œuvre des règles de connaissances expertes permettant d'évaluer et de détecter les escarres et les activités. * Nous avons mis en œuvre et évaluer le framework SeMoM en se basant sur deux expérimentations. La première basée sur le déploiement d'un système ciblant la détection des activités ADL dans un laboratoire d'expérimentation pour la santé (le Connected Health Lab). La seconde est basée sur le simulateur d'activités ADLSim développé par l'Université d'Oslo. Ce simulateur a été utilisé pour effectuer des tests de performances de notre solution en générant une quantité massive de données sur les activités d'une personne à domicile.Nowadays, the adoption of the Internet of Things (IoT) has received a considerable interest from both academia and industry. It provides enhancements in quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these "Things" are made available with heterogeneous vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were originally set up. In this thesis, we address these challenges in the context of healthcare applications considering how we transform raw data to cognitive knowledge and ontology-based information shared between IoT system components. With respect to heterogeneity and integration challenges, our main contribution is an ontology-based IoT architecture allowing the deployment of semantic IoT applications. This approach allows sharing of sensors observations, contextualization of data and reusability of knowledge and processed information. Specific contributions include: * Design of the Cognitive Semantic Sensor Network ontology (CoSSN) ontology: CoSSN aims at overcoming the semantic interoperability challenges introduced by the variety of sensors potentially used. It also aims at describing expert knowledge related to a specific domain. * Design and implementation of SeMoM: SeMoM is a flexible IoT architecture built on top of CoSSN ontology. It relies on a message oriented middleware (MoM) following the publish/subscribe paradigm for a loosely coupled communication between system components that can exchange semantic observation data in a flexible way. From the applicative perspective, we focus on healthcare applications. Indeed, specific approaches and individual prototypes are preeminent solutions in healthcare which straighten the need of an interoperable solution especially for patients with multiple affections. With respect to these challenges, we elaborated two case studies 1) bedsore risk detection and 2) Activities of Daily Living (ADL) detection as follows: * We developed extensions of CoSSN to describe each domain concepts and we developed specific applications through SeMoM implementing expert knowledge rules and assessments of bedsore and human activities. * We implemented and evaluated the SeMoM framework in order to provide a proof of concept of our approach. Two experimentations have been realized for that target. The first is based on a deployment of a system targeting the detection of ADL activities in a real smart platform. The other one is based on ADLSim, a simulator of activities for ambient assisted living that can generate a massive amount of data related to the activities of a monitored person

Context modelling for natural Human Computer Interaction applications in e-health

The conception of IoT (Internet of Things) is accepted as the future tendency of Internet among academia and industry. It will enable people and things to be connected at anytime and anyplace, with anything and anyone. IoT has been proposed to be applied into many areas such as Healthcare, Transportation，Logistics, and Smart environment etc. However, this thesis emphasizes on the home healthcare area as it is the potential healthcare model to solve many problems such as the limited medical resources, the increasing demands for healthcare from elderly and chronic patients which the traditional model is not capable of. A remarkable change in IoT in semantic oriented vision is that vast sensors or devices are involved which could generate enormous data. Methods to manage the data including acquiring, interpreting, processing and storing data need to be implemented. Apart from this, other abilities that IoT is not capable of are concluded, namely, interoperation, context awareness and security & privacy. Context awareness is an emerging technology to manage and take advantage of context to enable any type of system to provide personalized services. The aim of this thesis is to explore ways to facilitate context awareness in IoT. In order to realize this objective, a preliminary research is carried out in this thesis. The most basic premise to realize context awareness is to collect, model, understand, reason and make use of context. A complete literature review for the existing context modelling and context reasoning techniques is conducted. The conclusion is that the ontology-based context modelling and ontology-based context reasoning are the most promising and efficient techniques to manage context. In order to fuse ontology into IoT, a specific ontology-based context awareness framework is proposed for IoT applications. In general, the framework is composed of eight components which are hardware, UI (User Interface), Context modelling, Context fusion, Context reasoning, Context repository, Security unit and Context dissemination. Moreover, on the basis of TOVE (Toronto Virtual Enterprise), a formal ontology developing methodology is proposed and illustrated which consists of four stages: Specification & Conceptualization, Competency Formulation, Implementation and Validation & Documentation. In addition, a home healthcare scenario is elaborated by listing its well-defined functionalities. Aiming at representing this specific scenario, the proposed ontology developing methodology is applied and the ontology-based model is developed in a free and open-source ontology editor called Protégé. Finally, the accuracy and completeness of the proposed ontology are validated to show that this proposed ontology is able to accurately represent the scenario of interest