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95 lines
3.9 KiB
Markdown
95 lines
3.9 KiB
Markdown
# INTENT.md — Project Vantage Point
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## Purpose
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Project Vantage Point aims to establish a generic, extensible system for exploring, analyzing, and reasoning about dependency structures across arbitrary domains.
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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.
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## Core Idea
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Understanding complex systems requires more than visualizing connections — it requires the ability to:
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- shift perspective,
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- reduce complexity,
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- reveal structure,
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- and interpret relationships in context.
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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.
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This provides a practical implementation of *Network-Based Graph Models (NBGM)* based on the article
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"The Four-Level Nested Model Revisited: Blocks and Guidelines" by Miriah Meyer, Michael Sedlmair and Tamara Munzner
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detailing concepts of Tamara Munzner’s nested model explorations.
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We will try and use this as the foundation for a clean, research-grounded vocabulary.
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See: https://miriah.github.io/publications/nbgm-beliv.pdf
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## Scope of Intent
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The project focuses on building a **domain-agnostic dependency intelligence layer** that can be bound to specific domains through configuration rather than code changes.
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It is not limited to any specific application area. Intended domains include, but are not limited to:
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- Software architecture and code dependencies
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- Infrastructure and operational systems
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- Organizational and ownership structures
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- Product and capability models
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- Knowledge graphs and conceptual systems
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- Legal, economic, or argumentation networks
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## Guiding Principles
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### 1. Generic Core, Domain-Specific Interpretation
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The underlying graph model remains neutral. Meaning emerges through domain bindings, lenses, and interpretations.
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### 2. Perspective over Representation
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There is no single “correct” visualization. Different questions require different vantage points, layouts, and abstractions.
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### 3. Reduction over Exhaustiveness
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Clarity is achieved by filtering, aggregating, and focusing — not by rendering the entire graph at once.
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### 4. Provenance and Trust
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All relationships should carry information about origin, confidence, and freshness to support reliable reasoning.
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### 5. Explainability First
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Every visual element should be inspectable and explainable in terms of:
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- what it represents,
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- why it exists,
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- and how it was derived.
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### 6. Evolution Awareness
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Graphs are dynamic. The system should support comparison, drift detection, and temporal reasoning.
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### 7. Composability
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The system should allow composition of:
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- data sources,
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- graph transformations,
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- visual mappings,
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- and analytical lenses.
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## Intended Capabilities
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Vantage Point is intended to support:
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- Exploration of large, attribute-rich dependency graphs
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- Identification of structure (clusters, layers, cycles, hubs)
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- Analysis of impact, risk, and dependency chains
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- Comparison of graph states over time
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- Detection of inconsistencies and violations of intended structure
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- Domain-specific interpretation through configurable lenses
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## Non-Goals
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- Not a static diagramming tool
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- Not limited to a single domain or schema
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- Not dependent on a single visualization technique or layout
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- Not a passive data viewer without analytical capabilities
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## Vision
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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.
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It turns graphs from static representations into **interactive instruments for reasoning**.
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