vantage-point/INTENT.md

INTENT.md — Project Vantage Point

Purpose

Project Vantage Point aims to establish a generic, extensible system for exploring, analyzing, and reasoning about dependency structures across arbitrary domains.

At its core, Vantage Point treats systems as network-based graph models (NBGM) consisting of entities (nodes) and relationships (edges) enriched with attributes, provenance, and semantics. The project provides a unified way to inspect these structures and derive actionable understanding from them.

Core Idea

Understanding complex systems requires more than visualizing connections — it requires the ability to:

• shift perspective,
• reduce complexity,
• reveal structure,
• and interpret relationships in context.

Vantage Point is designed as a multi-perspective exploration environment where users can adopt different “vantage points” on the same underlying graph to answer domain-specific questions.

This provides a practical implementation of Network-Based Graph Models (NBGM) based on the article "The Four-Level Nested Model Revisited: Blocks and Guidelines" by Miriah Meyer, Michael Sedlmair and Tamara Munzner detailing concepts of Tamara Munzner’s nested model explorations.

We will try and use this as the foundation for a clean, research-grounded vocabulary.

See: https://miriah.github.io/publications/nbgm-beliv.pdf

Scope of Intent

The project focuses on building a domain-agnostic dependency intelligence layer that can be bound to specific domains through configuration rather than code changes.

It is not limited to any specific application area. Intended domains include, but are not limited to:

• Software architecture and code dependencies
• Infrastructure and operational systems
• Organizational and ownership structures
• Product and capability models
• Knowledge graphs and conceptual systems
• Legal, economic, or argumentation networks

Guiding Principles

1. Generic Core, Domain-Specific Interpretation

The underlying graph model remains neutral. Meaning emerges through domain bindings, lenses, and interpretations.

2. Perspective over Representation

There is no single “correct” visualization. Different questions require different vantage points, layouts, and abstractions.

3. Reduction over Exhaustiveness

Clarity is achieved by filtering, aggregating, and focusing — not by rendering the entire graph at once.

4. Provenance and Trust

All relationships should carry information about origin, confidence, and freshness to support reliable reasoning.

5. Explainability First

Every visual element should be inspectable and explainable in terms of:

• what it represents,
• why it exists,
• and how it was derived.

6. Evolution Awareness

Graphs are dynamic. The system should support comparison, drift detection, and temporal reasoning.

7. Composability

The system should allow composition of:

• data sources,
• graph transformations,
• visual mappings,
• and analytical lenses.

Intended Capabilities

Vantage Point is intended to support:

• Exploration of large, attribute-rich dependency graphs
• Identification of structure (clusters, layers, cycles, hubs)
• Analysis of impact, risk, and dependency chains
• Comparison of graph states over time
• Detection of inconsistencies and violations of intended structure
• Domain-specific interpretation through configurable lenses

Non-Goals

• Not a static diagramming tool
• Not limited to a single domain or schema
• Not dependent on a single visualization technique or layout
• Not a passive data viewer without analytical capabilities

Vision

Vantage Point becomes a foundational tool for making complex interconnected systems inspectable, understandable, and actionable by enabling users to observe them from the right perspective at the right level of abstraction.

It turns graphs from static representations into interactive instruments for reasoning.