Facilitates information exchange by defining and evaluating requirements and evidence using a context-neutral data model. Features include criteria definition, request scoring, and evidence-based assessments.
The connect-cccev project facilitates managing certification processes. It allows defining complex Requirements based on the standard CCCEV vocabulary, including associated InformationConcepts (specific data points) and needed Evidence (proofs). The system uses a Neo4j graph database to store these interconnected elements and provides an F2 API to create certifications and track their fulfillment by adding evidence and value
CCCEV gives us three main building blocks:
InformationRequirement), evaluation criteria (Criterion), or specific limitations (Constraint). For now, just think of it as the rule.Requirement needs, or that an Evidence provides.Requirement.Evidence. This document contains the "Date of Birth" (InformationConcept) needed to verify the "Under 26" (Requirement).
Each InformationConcept has its own score. The total score of a request is the average score of all its InformationConcepts.
Computation rules per field
(numberOfFulfilledEvidenceTypes / totalEvidenceTypes) * 50InformationConcept.InformationConcept has a specific type of data, called a DataUnit. This tells us if the information is a date, a number, text, true/false, etc.
* Example (Youth Pass): The InformationConcept "Date of Birth" has a DataUnit of "Date". An InformationConcept like "Age" would have a DataUnit of "Number".
* CCCEV Reference: Although not a direct CCCEV class, it aligns with the InformationConcept.type and expected value format. In connect-cccev, DataUnit provides this structure.Surfaced from shared tags and platforms — no rankings paid for.