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EXP-05CATEGORY: DataSTATUS: RESEARCH

Predictive Decision Engine

Low-latency anomaly detection and predictive operational analytics system for streaming enterprise data feeds.

EXPERIMENTAL OVERVIEW & OBJECTIVE

A research prototype that analyzes real-time streaming transactional telemetry to forecast operational bottlenecks before they occur.

The Research Challenge

Traditional batch analytics produce static historical reports, failing to alert operations teams to real-time anomalous patterns.

Engineering Approach

We train lightweight temporal transformer models on time-series telemetry streams to trigger automated alerts upon state drift.

SYSTEM ARCHITECTURE SPECIFICATION

Real-Time Stream Vectorization

Temporal Transformer Anomaly Detection

Sliding Window Metric Aggregation

Automated Alert Webhook Dispatcher

CURRENT BENCHMARK STATE

Benchmarking streaming latency under 100,000 telemetry events per second.

FUTURE ENGINEERING DIRECTION

Integrating automated root-cause analysis sub-agents.

TECHNOLOGY STACK & INFRASTRUCTURE
PythonPyTorchClickHouseRedisFastAPI

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