109 research outputs found
GI Systems for public health with an ontology based approach
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Health is an indispensable attribute of human life. In modern age,
utilizing technologies for health is one of the emergent concepts in
several applied fields. Computer science, (geographic) information
systems are some of the interdisciplinary fields which motivates this
thesis.
Inspiring idea of the study is originated from a rhetorical disease
DbHd: Database Hugging Disorder, defined by Hans Rosling at
World Bank Open Data speech in May 2010. The cure of this disease
can be offered as linked open data, which contains ontologies for
health science, diseases, genes, drugs, GEO species etc. LOD-Linked
Open Data provides the systematic application of information by
publishing and connecting structured data on the Web.
In the context of this study we aimed to reduce boundaries
between semantic web and geo web. For this reason a use case data is
studied from Valencia CSISP- Research Center of Public Health in
which the mortality rates for particular diseases are represented
spatio-temporally. Use case data is divided into three conceptual
domains (health, spatial, statistical), enhanced with semantic relations
and descriptions by following Linked Data Principles. Finally in order
to convey complex health-related information, we offer an
infrastructure integrating geo web and semantic web. Based on the
established outcome, user access methods are introduced and future
researches/studies are outlined
Semantic data ingestion for intelligent, value-driven big data analytics
In this position paper we describe a conceptual
model for intelligent Big Data analytics based on both semantic
and machine learning AI techniques (called AI ensembles). These
processes are linked to business outcomes by explicitly modelling
data value and using semantic technologies as the underlying
mode for communication between the diverse processes and
organisations creating AI ensembles. Furthermore, we show
how data governance can direct and enhance these ensembles
by providing recommendations and insights that to ensure the
output generated produces the highest possible value for the
organisation
A unified framework for managing provenance information in translational research
<p>Abstract</p> <p>Background</p> <p>A critical aspect of the NIH <it>Translational Research </it>roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the <it>provenance </it>metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists.</p> <p>Results</p> <p>We identify a common set of challenges in managing provenance information across the <it>pre-publication </it>and <it>post-publication </it>phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata:</p> <p>(a) Provenance <b>collection </b>- during data generation</p> <p>(b) Provenance <b>representation </b>- to support interoperability, reasoning, and incorporate domain semantics</p> <p>(c) Provenance <b>storage </b>and <b>propagation </b>- to allow efficient storage and seamless propagation of provenance as the data is transferred across applications</p> <p>(d) Provenance <b>query </b>- to support queries with increasing complexity over large data size and also support knowledge discovery applications</p> <p>We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for <it>Trypanosoma cruzi </it>(<it>T.cruzi </it>SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness.</p> <p>Conclusions</p> <p>The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis.</p
A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data
With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange
Mapping heterogeneous research infrastructure metadata into a unified catalogue for use in a generic virtual research environment
Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers
in data science by integrating tools for data discovery, data retrieval, workflow management
and researcher collaboration, often coupled with a specific computing infrastructure. Recently, the push
for better open data science has led to the creation of a variety of dedicated research infrastructures
(RIs) that gather data and provide services to different research communities, all of which can be used
independently of any specific VRE. There is therefore a need for generic VREs that can be coupled
with the resources of many different RIs simultaneously, easily customised to the needs of specific
communities. The resource metadata produced by these RIs rarely all adhere to any one standard
or vocabulary however, making it difficult to search and discover resources independently of their
providers without some translation into a common framework. Cross-RI search can be expedited by
using mapping services that harvest RI-published metadata to build unified resource catalogues, but
the development and operation of such services pose a number of challenges.
In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata
Portal, which uses X3ML mappings to build a single catalogue for describing data products and other
resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference
Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard
to structure its catalogued metadata. We consider the extent to which it addresses the challenges of
cross-RI search, particularly in the environmental and earth science domain, and how it can be further
augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic
search across multiple domains of discourse
Formal Description and Automatic Generation of Learning Spaces Based on Ontologies
AbstractA good virtual Learning Space (LS) should convey pertinent learning information to the visitors at the most adequate time and locations to favor their knowledge acquisition.Considering the consolidation of the internet and the improvement of the interaction, searching, and learning mechanisms, we propose a generic architecture, called CaVa, to create virtual Learning Spaces building up on cultural institution documents. More precisely, our proposal is to automatically create ontology-based virtual learning environments.Thus, to impart relevant learning materials to the virtual LS, we propose the use of ontologies to represent the key concepts and semantic relations in an user- and machine-understandable format. These concepts together with the data (extracted from the real documents) stored in a digital storage format (XML datasets, relational databases, etc.) are displayed in an ontology-based learning space that enables the visitors to use the available features and tools to learn about a specific domain.According to the approach here discussed, each desired virtual LS must be specified rigorously through a domain specific language (DSL) that was designed and implemented.To validate the proposed architecture, three case studies will be used as instances of CaVa architecture
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Novel processes for smart grid information exchange and knowledge representation using the IEC common information model
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge
representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but
also because information is now becoming recognised as the âintegral componentâ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid
contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud
computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations.
The second research contribution reports on a novel methodology for a CIM-based
model repository to align PSA representations and provide a
knowledge resource for building utility business intelligence of the grid.
The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the
USA.Dr. Martin Bradley on behalf of National Grid Plc. and the Engineering and Physical
Sciences Research Council (EPSRC
Offshore Wind Data Integration
Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 2014Using renewable energy to meet the future electricity consumption and to reduce
environmental impact is a significant target of many countries around the world.
Wind power is one of the most promising renewable energy technologies. In particular,
the development of offshore wind power is increasing rapidly due to large
areas of wind resources. However, offshore wind is encountering big challenges
such as effective use of wind power plants, reduced cost of installation as well as
operation and maintenance (O&M).
Improved O&M is likely to reduce the hazard exposure of the employees, increase
income, and support offshore activities more efficiently. In order to optimize the
O&M, the importance of data exchange and knowledge sharing within the offshore
wind industry must be realized. With more data available and accessible, it is possible
to make better decisions, and thereby improve the recovery rates and reduce
the operational costs.
This dissertation proposes a holistic way of improving remote operations for offshore
wind farms by using data integration. Particularly, semantics and integration
aspects of data integration are investigated. The research looks at both theoretical
foundations and practical implementations.
As the outcome of the research, a framework for data integration of offshore wind
farms has been developed. The framework consists of three main components: the
semantic model, the data source handling, and the information provisioning. In
particular, an offshore wind ontology has been proposed to explore the semantics
of wind data and enable knowledge sharing and data exchange. The ontology is
aligned with semantic sensor network ontology to support management of metadata
in smart grids. That is to say, the ontology-based approach has been proven to be
useful in managing data and metadata in the offshore wind and in smart grids. A
quality-based approach is proposed to manage, select, and provide the most suitable
data source for users based upon their quality requirements and an approach to
formally describing derived data in ontologies is investigated
Integrating Spatial Data Linkage and Analysis Services in a Geoportal for China Urban Research
Many geoportals are now evolving into online analytical environments, where large amounts of data and various analysis methods are integrated. These spatiotemporal data are often distributed in different databases and exist in heterogeneous forms, even when they refer to the same geospatial entities. Besides, existing open standards lack sufficient expression of the attribute semantics. Client applications or other services thus have to deal with unrelated preprocessing tasks, such as data transformation and attribute annotation, leading to potential inconsistencies. Furthermore, to build informative interfaces that guide users to quickly understand the analysis methods, an analysis service needs to explicitly model the method parameters, which are often interrelated and have rich auxiliary information. This work presents the design of the spatial data linkage and analysis services in a geoportal for China urban research. The spatial data linkage service aggregates multisource heterogeneous data into linked layers with flexible attribute mapping, providing client applications and services with a unified access as if querying a big table. The spatial analysis service incorporates parameter hierarchy and grouping by extending the standard WPS service, and dataâdependent validation in computation components. This platform can help researchers efficiently explore and analyze spatiotemporal data online.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110740/1/tgis12084.pd
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