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Analyze Artifacts defines profiles supporting the analysis of artifacts utilizing semantic queries, reasoning, rules, and rulesdata mediation.

In a diverse information environment, semantics must be used to clearly indicate the meaning of data. This requirement is expected to be addressed by the Semantic Infrastructure, although there will be a touchpoint between the caGrid 2.0 platform and the Semantic Infrastructure to annotate data with semantics. Integration with the Semantic Infrastructure will enable reasoning, semantic query, data mediation (for example, ad hoc data transformation) and other powerful capabilities. Data semantics are captured in the Semantic Infrastructure and the platform will leverage the Semantic Infrastructure interfaces for reasoning and analysis.

Link to use case satisfied from caGRID 2.0 Roadmap: The oncologist accesses the TCGA database to search for de-identified glioblastoma tumor data that is similar to the patient data exported from the hospital medical record. During this search, the semantics of the data fields are leveraged to indicate matches between TCGA data fields and the hospital medical record data fields.

Two families of rules have been identified by stakeholders. Rules related to workflow and business processes that would be executed by a general purpose rules engine (such as Drools, JESS) and rules specific to semantic reasoning (such as SWRL, SPARQL) that would be used within semantic technologies.

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• Develop a translation into formal semantics and computable abstraction and representation of policies and regulations imposed on a federal, state, organizational and institutional level. CaBIG standards, tools, best practices should make it easy or at least easier to be compliant. It should therefore aid the community in acknowledging, interpreting and applying the relevant regulations, inform the legislators on the success/value of the regulations and foster constant improvement
• Incorporate a rules engine
• Adopt a common syntax and a structure for rules which utilize data elements as their facts and conclusions. Adoption of a shared syntax and structure by all members of the caBIG community will facilitate exchange of executable knowledge and diminish the need for extensive or resource intensive learning efforts on the part of adopters of such a system.
• Adopt a common model for rule structure. The proposed structure for rules should support the rule as an object. This has significance in caBIG as it will allow the rules to be accessed via an interface and this will also allow the different components of rules to be treated as data elements.
• Maintain a rules repository.

For semantic services;

• Support a semantic rules capability, such as SWRL or rules expressed in SPARQL
• Support a semantic reasoner that can use those rules in reasoning / inferencing

Link to use case satisfied from caGRID 2.0 Roadmap: The oncologist accesses the TCGA database to search for de-identified glioblastoma tumor data that is similar to the patient data exported from the hospital medical record. During this search, the semantics of the data fields are leveraged to indicate matches between TCGA data fields and the hospital medical record data fields.

Functional Profile

• 5.2.1.2.1 - Analysis Sept. 6, 2010 Data semantics are captured in the Semantic Infrastructure and the platform will leverage the Semantic Infrastructure interfaces for analysis.
• 5.2.1.2.2 - Reasoning Sept. 6, 2010 Integration with the Semantic Infrastructure will enable reasoning.
• 5.2.1.2.3 - Rules Sept. 6, 2010 Two families of rules have been identified by stakeholders. Rules related to workflow and business processes that would be executed by a general purpose rules engine (such as Drools, JESS) and rules specific to semantic reasoning (such as SWRL, SPARQL) that would be used within semantic technologies. A Rule is a prescribed guide for carrying out activities and processes leading to desired results, e.g. the operational realization of policies. A Regulation is a mandated process or the specific details that derive from the interpretation of Rules and lead to measureable quantities against which compliance can be measured. Policy is made operational through the promulgating and implementing of Rules and Regulations