Analyze Services defines profiles for service analysis, providing support for determining aspects such as service interaction dependencies, service reuse, service conformance assessment, heterogeneous data interchange, and service collaboration compatibility.
The use of well defined service metadata promotes discovery and reuse of services during design and run time. Service metadata includes information about service interactions and dependencies. It also includes a classification scheme for organizing services based on business objectives, domain, and usage. It links services to all the supporting artifacts in the specification and provides a placeholder for conformance statements. This enables better reuse across the enterprise and eliminates redundancy.
Information and behavioral models, in conjunction with discovery mechanisms, mediation, classification, traceability from requirement to operation; and interaction logs, enable comprehensive analysis to be performed throughout the lifecycle of artifacts, from design through run-time implementation.
The semantic models managed by the Semantic Infrastructure enable enhanced reasoning.
A capability to analyze data was explicitly cited as a critical goal by Clinical Data Interchange Standards Consortium (CDISC) and caBIG Clinical Information Suite stakeholders. From the perspective of the Knowledge Repository, this requirement should perhaps be entitled “Enable Data Integration through Model Alignment,” since it allows users to provide the metadata necessary to enable integration of heterogeneous data sets. Requirements include:
- Query and retrieve Value Sets
- Support integrative analysis across multiple disciplines
- Query and aggregate data across organizations, data sets, time, and geographies.
- Integrate clinical trial data with health record data. By multiple data sets CDISC means different clinical trials, data sets from different Electronic Health Record (EHR) systems, and the interoperability of clinical trial and EHR data sets. At a minimum the Knowledge Repository should be able to identify those data sets that are constructed from or map to the Knowledge Repository's information models, model elements, and alignment assertions among these models. Thus, an approved alignment between a local information model and (for example) a specific CDISC standard would be an assertion that new models could map to. This linkage from the Knowledge Repository to specific data sets that instantiate information models sets the stage for full model analysis.
Functional Profile
- 5.2.1 - Analyze Information and behavioral models, in conjunction with discovery mechanisms, mediation, classification, traceability from requirement to operation, and interaction logs, enable comprehensive analysis to be performed through-out the life-cycle of artifacts , from design through run-time implementation.
- 5.2.2 - Reason The semantic models managed by the Semantic Infrastructure enable enhanced reasoning.
