6,372 research outputs found
Use-cases on evolution
This report presents a set of use cases for evolution and reactivity for data in the Web and
Semantic Web. This set is organized around three different case study scenarios, each of them
is related to one of the three different areas of application within Rewerse. Namely, the scenarios
are: “The Rewerse Information System and Portal”, closely related to the work of A3
– Personalised Information Systems; “Organizing Travels”, that may be related to the work
of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources”
related to the work of A2 – Towards a Bioinformatics Web
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 Semantic Framework for Enabling Radio Spectrum Policy Management and Evaluation
Because radio spectrum is a finite resource, its usage and sharing is
regulated by government agencies. These agencies define policies to manage
spectrum allocation and assignment across multiple organizations, systems, and
devices. With more portions of the radio spectrum being licensed for commercial
use, the importance of providing an increased level of automation when
evaluating such policies becomes crucial for the efficiency and efficacy of
spectrum management. We introduce our Dynamic Spectrum Access Policy Framework
for supporting the United States government's mission to enable both federal
and non-federal entities to compatibly utilize available spectrum. The DSA
Policy Framework acts as a machine-readable policy repository providing policy
management features and spectrum access request evaluation. The framework
utilizes a novel policy representation using OWL and PROV-O along with a
domain-specific reasoning implementation that mixes GeoSPARQL, OWL reasoning,
and knowledge graph traversal to evaluate incoming spectrum access requests and
explain how applicable policies were used. The framework is currently being
used to support live, over-the-air field exercises involving a diverse set of
federal and commercial radios, as a component of a prototype spectrum
management system
A Personalized Framework for Trust Assessment
The number of computational trust models has been increasing quickly in recent years yet their applications for automating trust evaluation are still limited. The main obstacle is the difficulties in selecting a suitable trust model and adapting it for particular trust modeling requirements, which varies greatly due to the subjectivity of human trust. The Personalized Trust Framework (PTF) presented in this paper aims to address this problem by providing a mechanism for human users to capture their trust evaluation process in order for it to be replicated by computers. In more details, a user can specify how he selects a trust model based on information about the subject whose trustworthiness he needs to evaluate and how that trust model is configured. This trust evaluation process is then automated by the PTF making use of the trust models flexibly plugged into the PTF by the user. By so doing, the PTF enable users reuse and personalize existing trust models to suit their requirements without having to reprogram those models
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