8,282 research outputs found
A Semantic Grid Oriented to E-Tourism
With increasing complexity of tourism business models and tasks, there is a
clear need of the next generation e-Tourism infrastructure to support flexible
automation, integration, computation, storage, and collaboration. Currently
several enabling technologies such as semantic Web, Web service, agent and grid
computing have been applied in the different e-Tourism applications, however
there is no a unified framework to be able to integrate all of them. So this
paper presents a promising e-Tourism framework based on emerging semantic grid,
in which a number of key design issues are discussed including architecture,
ontologies structure, semantic reconciliation, service and resource discovery,
role based authorization and intelligent agent. The paper finally provides the
implementation of the framework.Comment: 12 PAGES, 7 Figure
Enhancing Workflow with a Semantic Description of Scientific Intent
Peer reviewedPreprin
A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID
An important trend in modern medicine is towards individualisation of healthcare to tailor
care to the needs of the individual. This makes it possible, for example, to personalise
diagnosis and treatment to improve outcome. However, the benefits of this can only be fully
realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data,
analysis algorithms, knowledge and expertise). Potentially, grid can play an important role
in this by allowing sharing of resources and expertise to improve the quality of care. The
integration of grid and the new concept of bioprofile represents a new topic in the healthgrid
for individualisation of healthcare.
A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's
current and past bio-history, biopatterns and prognosis. It combines not just data, but also
analysis and predictions of future or likely susceptibility to disease, such as brain diseases
and cancer. The creation and use of bioprofile require the support of a number of healthcare
and ICT technologies and techniques, such as medical imaging and electrophysiology and
related facilities, analysis tools, data storage and computation clusters. The need to share
clinical data, storage and computation resources between different bioprofile centres creates
not only local problems, but also global problems.
Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the
use and management of heterogeneous IT resources at different bioprofile centres. Grid as an
emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including
discovery, access, monitoring and allocation of distributed bioprofile databases, computation
resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the
grid and bioprofile technologies together in order to offer an advanced distributed bioprofile
environment to support individualized healthcare remains.
The aim of this project is to develop a framework for one of the key meta-level bioprofile
applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile
analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles).
Analysis makes it possible, for example, to extract markers from data for diagnosis and to
assess individual's health status. The framework provides a basis for a "grid-based" solution
to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions
of the thesis are fourfold:
A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture
is fundamental to the development of any ICT systems. The architecture creates a
meaiis for categorisation, determination and organisation of core grid components to
support the development and use of grid for bioprofile analysis;
B. A service model for bioprofile analysis over grid. The service model proposes a
service design principle, a service architecture for bioprofile analysis over grid, and
a distributed EEG analysis service model. The service design principle addresses
the main service design considerations behind the service model, in the aspects of
usability, flexibility, extensibility, reusability, etc. The service architecture identifies
the main categories of services and outlines an approach in organising services to
realise certain functionalities required by distributed bioprofile analysis applications.
The EEG analysis service model demonstrates the utilisation and development of
services to enable bioprofile analysis over grid;
C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two
grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing
grid middleware tools. They provide essential experimental platforms for research in
bioprofiling over grid. The work here demonstrates how resources, grid middleware
and services can be utilised, organised and implemented to support distributed EEG
analysis for early detection of dementia. The distributed Electroencephalography
(EEG) analysis environment can be used to support a variety of research activities in
EEG analysis;
D. A scheme for organising multiple (heterogeneous) descriptions of individual grid
entities for knowledge representation of grid. The scheme solves the compatibility
and adaptability problems in managing heterogeneous descriptions (i.e. descriptions
using different languages and schemas/ontologies) for collaborated representation of
a grid environment in different scales. It underpins the concept of bioprofile analysis
over grid in the aspect of knowledge-based global coordination between components
of bioprofile analysis over grid
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Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data
resources still remains a key challenge in Grid infrastructures. Today's
mainstream Grid technologies hold the promise to meet this challenge in a
systematic manner, making data applications more scalable and manageable. The
thesis conducts a thorough investigation of the problem, the state of the art, and
the related technologies, and proposes an Architecture for Semantic Integration of
Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a
simple mechanism for the interoperability of heterogeneous data sources in order
to extract or discover information regardless of their different semantics. The
constituent technologies of this architecture include Globus Toolkit (GT4) and
OGSA-DAI (Open Grid Service Architecture Data Integration and Access)
alongside other web services technologies such as XML (Extensive Markup
Language). To show this, the ASIDS architecture was implemented and tested in a
realistic setting by building an exemplar application prototype on a HealthGrid
(pilot implementation).
The study followed an empirical research methodology and was informed by
extensive literature surveys and a critical analysis of the relevant technologies and
their synergies. The two literature reviews, together with the analysis of the
technology background, have provided a good overview of the current Grid and
HealthGrid landscape, produced some valuable taxonomies, explored new paths
by integrating technologies, and more importantly illuminated the problem and
guided the research process towards a promising solution. Yet the primary
contribution of this research is an approach that uses contemporary Grid
technologies for integrating heterogeneous data resources that have semantically
different. data fields (attributes). It has been practically demonstrated (using a
prototype HealthGrid) that discovery in semantically integrated distributed data
sources can be feasible by using mainstream Grid technologies, which have been
shown to have some Significant advantages over non-Grid based approaches
Mobile Edge Computing Empowers Internet of Things
In this paper, we propose a Mobile Edge Internet of Things (MEIoT)
architecture by leveraging the fiber-wireless access technology, the cloudlet
concept, and the software defined networking framework. The MEIoT architecture
brings computing and storage resources close to Internet of Things (IoT)
devices in order to speed up IoT data sharing and analytics. Specifically, the
IoT devices (belonging to the same user) are associated to a specific proxy
Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes
the IoT data (generated by its IoT devices) in real-time. Moreover, we
introduce the semantic and social IoT technology in the context of MEIoT to
solve the interoperability and inefficient access control problem in the IoT
system. In addition, we propose two dynamic proxy VM migration methods to
minimize the end-to-end delay between proxy VMs and their IoT devices and to
minimize the total on-grid energy consumption of the cloudlets, respectively.
Performance of the proposed methods are validated via extensive simulations
Adaptive service discovery on service-oriented and spontaneous sensor systems
Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief
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