World Layer Analysis Data Algorithms
WLADA Engine
The signal processing and analysis system powering World Signal Map. WLADA fuses multi-layer data ingestion, novelty scoring, intensity calculation, correlation analysis, and lifecycle management into a unified pipeline for real-time global signal intelligence.
Ingests signals from four global layers through dedicated Python collectors tracking Google Trends, financial markets, BGP/infrastructure events, and news wires.
Multi-pass reasoning detects weak signals, computes novelty scores with explainable reasoning, and maps propagation patterns across regions.
Structured ingest and normalization pipelines powered by Redis Streams. Aligns live telemetry with lifecycle states from detection through archival.
Outputs cross-layer correlation tracing, forensic dependency analysis, timeline reconstruction, and command-ready visibility on the 3D globe.
Why WLADA
The analytical core behind World Signal Map's planetary intelligence.
WLADA is not a standalone product — it is the signal processing engine that powers World Signal Map. Built in Python 3.11+, it handles signal normalization, coordinate resolution, clustering, and feature extraction through a dedicated processing pipeline.
Novelty Scoring
Every signal receives a novelty score with explainable reasons. Intensity tracked on a 0.0–1.0 scale with confidence levels.
Signal Lifecycle
Signals progress through defined states: detected, rising, active, stabilizing, fading, archived. Each transition timestamped.
Correlation
Cross-layer correlation traces connections across geographic, temporal, semantic, and entity dimensions between signals.
Core Pipeline
From raw signal ingestion to rendered globe visualization.
Python collectors ingest signals from Google Trends, financial markets, BGP events, Reddit, and news wires.
Redis Streams pipeline normalizes, clusters, and routes signals through the novelty scoring engine.
Cross-layer correlation analysis traces connections and computes dependency graphs between signals.
WebSocket streaming with 100ms batching delivers scored signals to the Three.js 3D globe interface.
Python Engine
Core signal engine built in Python 3.11+ with dedicated processing pipeline.
Handles signal normalization, coordinate resolution, clustering, and feature extraction. Collectors run independently for each data source.
Real-Time Pipeline
Redis Streams ensure ordered, reliable event processing at scale.
WebSocket streaming with 100ms batching delivers signals to the globe. PostgreSQL stores 33+ Prisma models across signal, auth, and ML domains.
ML Infrastructure
ML data layer with feature extraction and pipeline status monitoring.
Designed for continuous model improvement on signal classification and prediction. Admin panel monitors pipeline status and system health metrics.
World Signal Map
See WLADA Engine in action on the World Signal Map globe.
WLADA powers World Signal Map's signal detection, lifecycle tracking, and correlation analysis across Human, Infrastructure, Financial, and Telecom layers.