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Synthesizing "Super Alerts" with Graph Theory
Signal compression for alert floods: statistical fingerprints + community detection + a single narrative.
The problem
Continuous, real-time monitoring floods operations teams with hundreds of overlapping alerts for a single underlying issue.
Even strong teams lose time correlating, deduplicating, and translating fragments into a real incident story.
What I built / changed
- Developed a "signal compression" engine for alert fatigue reduction.
- Generated a statistical fingerprint for every incoming alert using information entropy.
- Used graph community detection to cluster alerts sharing the same underlying signature.
- Used an LLM to synthesize each cluster into a single incident narrative.
Result
Instead of receiving 50 fragmented notifications, the team gets one "Super Alert" that explains the root cause.
Stack / concepts
Graph TheoryInformation TheoryLLM Synthesis
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