NSI: Dynamic Semantic Graphs for Real-Time and Explainable Operational Analytics

Most operational analytics today oscillates between simplistic dashboards and opaque ML pipelines, both of which struggle with regime shifts and explainability. We introduce NSI, a paradigm based on dynamic semantic graphs that continuously represent streaming data as evolving scenes and motifs. NSI detects scene shifts through changes in graph energy and structure, and provides actionable outputs in a WHAT/WHY/HOW format: what changed, why (drivers and contributing features), and how relationships reorganized. The approach requires no supervised labels, integrates with heterogeneous data streams, and yields domain-agnostic, interpretable signals for operators in retail pricing, industrial telemetry, finance, and fraud detection. This whitepaper outlines the architecture, outputs, and interpretation guide of NSI, positioning it as a universal observer for real-time, explainable analytics.