This section provides an overview of the proposed architecture, which includes a set of core services and tools. Section 5.2 - Overview of Core Semantic Infrastructure Capabilities and Services Profile Sept. 6, 2010 summarizes the profile of the solution with mapping to appropriate requirements and use cases. Section 5.3 - Tools for Semantic Infrastructure 2.0 Sept. 6, 2010 provides an end user's view of the tools. Section 5.4 - User Workflows in Semantic Infrastructure 2.0 Sept. 6, 2010 describes workflows, and section 5.5 - Tie-in with Terminology and Platform Sept. 6, 2010 describes integration with the platform and terminology.
The image below gives an overall view of the components required for Semantic Infrastructure 2.0.
Note
A description of the relationship among components in the diagram will be provided.
Legend for Diagram of Architecture of Semantic Infrastructure 2.0
Component Name |
Description |
|---|---|
Box name:Metadata Management |
Provides for the expression, capture, storage, and retrieval of metadata, including models, datatype usage, terminology requirements and contextual aspects of model components (Note: The Metadata and Model Repositories logically reside in this area. This varies from but is in line with the solicitation as all repositories will be developed.) |
Box name:Metadata Stores |
Is concerned with providing context and meaning to data and will include data elements, terminology references, and informational knowledge. |
Box name:Model Management |
Models constitute the expression of metadata elements for a particular domain or problem set. Model Management allows elements to be collected and related to provide for useful solutions. Models are described using OMGs XMI language, providing for the foundations of common tooling and expression. As important as XMI, are the human-readable models expressed in familiar UML. These models contain essential elements for solutions to function. |
Box name: HL7 v3 RIM meta Infomation |
The precision afforded by HL7 V3 is afforded by models that are used to characterize data as well as metadata for a model's elements. Managing these models as Metadata allows use and reuse in the day-to-day operations of NCI, allowing precision and accuracy in exchanging data within the BIG Health scope |
Box name: Knowledge Management Service Bus |
The Service Bus allows for a versatile, flexible approach to providing additional functionality that leverages the existing components of the semantic infrastructure. It is intended to leverage and extend other service bus solutions in service at NCI and to allow the integration of federated artifacts into NCI administered semantic infrastructure space. The Knowledge Management Service Bus exposes direct artifact retrieval as well as discovery services to identify knowledge components that may be of interest to applications. (Note: The Knowledge Service, Metadata Repository(MDR), and other services reside here and we consider the Model Service logically part of the MDR Service.) |
Box name:MDA Services |
Provides for an extensible platform based upon semantic web technologies to support registration, search and retrieval of models within the knowledge repository. |
Box name:Knowledge Services |
Provides for an extensible platform based upon semantic web technologies to support registration of, search for and retrieval of knowledge components and the analysis of such components based upon their metadata characteristics. |
Box name:Transformational Services |
The Transformational Services provide for an extensible platform, based on Semantic Web Technologies to support enterprise initiatives to leverage deployed data and metadata by providing tools to extend, scale, or query existing data stores. |
Box name:Knowledge Management |
Provides for the expression, capture, storage, and retrieval of knowledge concerning usage of metadata and data in the deployed architecture. Knowledge includes concepts and usage, both of which may be expressed using formal ontologies. |
Box name:Semantic Knowledge Store |
The Semantic Knowledge Store leverages RDF Triple Stores to provide for concepts and their relationships. |
Box name:Knowledge Analysis |
Knowledge Analysis provides for the rules and the reasoning engines that leverage knowledge captured in the Knowledge Management stores to extend scientific and clinical research, as well as provides the basis for an extensible rule-driven alert network. |
Box Name:Terminology Management |
Allows for the expression, storage, and retrieval of terminologies and vocabulary elements to support models, usage, metadata, and knowledge management. |
