Understanding Hierarchical Model Extraction

QueryBase Hierarchical Queries enable teams to extract rich architectural models from Visual Paradigm Cloud and convert them into structured, developer-friendly data formats. By organizing model data into a clean tree structure, QueryBase makes it effortless to analyze, transform, and integrate your modeling assets into external systems.

What is Hierarchical Model Extraction?

Visual models are inherently hierarchical. A system model contains multiple diagrams; diagrams contain shapes, entities, and connectors; and each shape possesses distinct properties such as names, stereotypes, custom tags, and documentation notes. Hierarchical Model Extraction is QueryBase’s built-in engine for parsing these nested relationships and flattening them into structured JSON payloads. Instead of requiring manual data entry or complex custom API integrations, users can extract well-defined tree structures directly from their web browser.

The Standard Extraction Structure

Hierarchical queries strictly enforce a logical multi-tier data model:
Project > Diagram > Diagram Element > Properties
This uniform mapping guarantees that every extracted model payload follows a predictable, easy-to-parse schema regardless of diagram type:
  • Project Level: The top-level wrapper defining repository metadata, project name, version, and unique IDs.
  • Diagram Level: Contains the subset of targeted diagrams (e.g., Use Case Diagrams, BPMN workflows, ERDs) filtered by your query criteria.
  • Diagram Element Level: Lists all shapes, actors, classes, tasks, or connectors present within each diagram.
  • Properties Level: Contains explicit metadata attributes chosen for inclusion, such as names, descriptions, stereotypes, and custom tagged values.

Example Structured JSON Output

Below is a simplified example of how QueryBase structures an extracted Use Case Diagram model into clean JSON: 
{
  "workspace": "dshv9sdg",
  "projects": [
    {
      "id": "PRJ-90210",
      "name": "E-Commerce System",
      "diagrams": [
        {
          "id": "DIAG-101",
          "name": "Checkout Process",
          "type": "UseCaseDiagram",
          "imageUrl": "https://....",
          "diagramElements": [
            {
              "id": "ELEM-001",
              "name": "Customer",
              "stereotypes": ["PrimaryActor"],
              "description": "Registered user performing online purchases."
            },
            {
              "id": "ELEM-002",
              "name": "Process Payment",
              "type": "UseCase",
              "status": "Approved"
            }
          ]
        }
      ]
    }
  ]
}

Common Business & Engineering Use Cases

Structured hierarchical extraction turns static design artifacts into actionable data for downstream workflows:

📄 Automated Documentation Generation

Feed structured JSON into static site generators or publishing tools (e.g., Hugo, Sphinx, Confluence) to maintain automated, living software documentation.

⚙️ CI/CD & Code Generation Pipelines

Extract data models and class specifications directly into build scripts to automatically generate API interfaces, DTOs, database migrations, or configuration files.

📊 Compliance & Governance Reporting

Extract properties, custom tags, and security levels into data analytics tools or Excel to run enterprise risk and regulatory compliance checks.

Extraction Tier Overview

Tier Level Description Typical Extracted JSON Attributes
Project Root container in Cloud Workspace id, name, author, lastModified
Diagram Visual view or canvas container id, name, type (e.g., BPMN, UML, ERD)
Diagram Element Individual shape, model, or line id, name, elementType (e.g., Class, Actor, Task)
Properties Detailed attributes and tags stereotype, documentation, customTags
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