PatternDB Methodology Whitepaper
Systemores LLC • Version 2.4.0 • Public Specification
1. The De-Aliasing Hypothesis
Modern regulatory compliance bodies—such as the National Transportation Safety Board (NTSB), the Food and Drug Administration (FDA MAUDE), the Securities and Exchange Commission (SEC EDGAR), and the Occupational Safety and Health Administration (OSHA)—publish vast repositories of disaster post-mortems. However, these repositories exist in hermetically sealed domain silos. An aerospace flight control runaway is indexed using aerodynamic pitch angles, while a high-frequency trading flash crash is indexed using limit-order book depth.
PatternDB operates on the hypothesis that all operational and institutional disasters are physical or mathematical manifestations of a finite set of invariant behavioral failure archetypes. By stripping domain jargon and converting actors into decision nodes, sensors into telemetry channels, and regulations into constraint boundaries, cross-domain topological congruence emerges.
2. The 4-Axis Signature Vector
Every structural failure pattern P in PatternDB is parameterized by a normalized 4-dimensional signature vector v in [0.0, 1.0]^4:
3. Algorithmic Twin Matching Formulation
Cross-domain twin discovery is performed using normalized cosine similarity across the 4-dimensional signature space. Given two structural failure manifestations with vectors $u$ and $v$:
A pair of manifestations constitutes a Topological Twin Match if:
- Cosine similarity exceeds 0.85 (85% congruency).
- Euclidean distance d(u,v) = sqrt(sum(u_i - v_i)^2) <= 0.35.
- The manifestations originate from disjoint industrial sectors (Domain_A != Domain_B).
4. Primitive Extraction & De-Aliasing Grammar
Raw disaster post-mortems from regulatory archives are parsed through the PatternDB de-aliasing dictionary, which replaces domain jargon with six fundamental systems engineering primitives: