An active, ontology-driven network service for Internet collaboration

Web portals have emerged as an important means of collaboration on the WWW, and the integration of ontologies promises to make them more accurate in how they serve users’ collaboration and information location requirements. However, web portals are essentially a centralised architecture resulting in difficulties supporting seamless roaming between portals and collaboration between groups supported on different portals. This paper proposes an alternative approach to collaboration over the web using ontologies that is de-centralised and exploits content-based networking. We argue that this approach promises a user-centric, timely, secure and location-independent mechanism, which is potentially more scaleable and universal than existing centralised portals

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Scalable software framework for real-time data processing in the railway environment

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Scalable software framework for real-time data processing in the railway environment

Background: Ticks are obligate haematophagous ectoparasites of vertebrates and frequently parasitize avian species that can carry them across continents during their long-distance migrations. Ticks may have detrimental effects on the health state of their avian hosts, which can be either directly caused by blood-draining or mediated by microbial pathogens transmitted during the blood meal. Indeed, ticks host complex microbial communities, including bacterial pathogens and symbionts. Midichloria bacteria (Rickettsiales) are widespread tick endosymbionts that can be transmitted to vertebrate hosts during the tick bite, inducing an antibody response. Their actual role as infectious/pathogenic agents is, however, unclear. Methods: We screened for Midichloria DNA African ticks and blood samples collected from trans-Saharan migratory songbirds at their arrival in Europe during spring migration. Results: Tick infestation rate was 5.7%, with most ticks belonging to the Hyalomma marginatum species complex. Over 90% of Hyalomma ticks harboured DNA of Midichloria bacteria belonging to the monophylum associated with ticks. Midichloria DNA was detected in 43% of blood samples of avian hosts. Tick-infested adult birds were significantly more likely to test positive to the presence of Midichloria DNA than non-infested adults and second-year individuals, suggesting a long-term persistence of these bacteria within avian hosts. Tick parasitism was associated with a significantly delayed timing of spring migration of avian hosts but had no significant effects on body condition, whereas blood Midichloria DNA presence negatively affected fat deposits of tick-infested avian hosts. Conclusions: Our results show that ticks effectively transfer Midichloria bacteria to avian hosts, supporting the hypothesis that they are infectious to vertebrates. Bird infection likely enhances the horizontal spread of these bacteria across haematophagous ectoparasite populations. Moreover, we showed that Midichloria and tick parasitism have detrimental non-independent effects on avian host health during migration, highlighting the complexity of interactions involving ticks, their vertebrate hosts, and tick-borne bacteria

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy