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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
Data integration for XML based on semantic knowledge
Reconciling of knowledge from multiple heterogeneous data sources has been a major focus of database research for more than a decade.As a standard for exchanging business data on the WWW, XML should provide the ability of expressing data and semantics among them. Since most of application data are stored in relational databases due to its popularity and rich development experiences over it.Therefore, how to
provide a proper mapping approach from relational
model to XML model becomes the major research
problem in the field of current information exchanging, sharing and integration..The model needs to be integrated and at the same time maintain the semantic knowledge among the data. The aim of this paper is to provide an overview for XML based data integration on semantic knowledge.At the end of the paper, we review
some methodologies from existing literature
Annotation of SBML Models Through Rule-Based Semantic Integration
*Motivation:* The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort. Here, we present a method using off-the-shelf semantic web technology which enables this process: the heterogeneous data sources are first syntactically converted into ontologies; these are then aligned to a small domain ontology by applying a rule base. Integrating resources in this way can accommodate multiple formats with different semantics; it provides richly modelled biological knowledge suitable for annotation of SBML models.
*Results:* We demonstrate proof-of-principle for this rule-based mediation with two use cases for SBML model annotation. This was implemented with existing tools, decreasing development time and increasing reusability. This initial work establishes the feasibility of this approach as part of an automated SBML model annotation system.
*Availability:* Detailed information including download and mapping of the ontologies as well as integration results is available from "http://www.cisban.ac.uk/RBM":http://www.cisban.ac.uk/RB
Enabling semantic queries across federated bioinformatics databases
MOTIVATION: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases.
RESULTS: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface
BIM-to-BRICK: Using graph modeling for IoT/BMS and spatial semantic data interoperability within digital data models of buildings
The holistic management of a building requires data from heterogeneous
sources such as building management systems (BMS), Internet-of-Things (IoT)
sensor networks, and building information models. Data interoperability is a
key component to eliminate silos of information, and using semantic web
technologies like the BRICK schema, an effort to standardize semantic
descriptions of the physical, logical, and virtual assets in buildings and the
relationships between them, is a suitable approach. However, current data
integration processes can involve significant manual interventions. This paper
presents a methodology to automatically collect, assemble, and integrate
information from a building information model to a knowledge graph. The
resulting application, called BIM-to-BRICK, is run on the SDE4 building located
in Singapore. BIM-to-BRICK generated a bidirectional link between a BIM model
of 932 instances and experimental data collected for 17 subjects into 458 BRICK
objects and 1219 relationships in 17 seconds. The automation of this approach
can be compared to traditional manual mapping of data types. This scientific
innovation incentivizes the convergence of disparate data types and structures
in built-environment applications
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