Problem & Context
A specific “fire” event needed to be reliably detected from system logs, but the raw logs only exposed low level signals and status changes. The goal was to infer when the event occurred with at least 99% confidence based on patterns in the data, so that downstream systems could respond automatically.
Scope of Work
- Analyzed historical logs to identify consistent patterns in the signals leading up to and during a fire event.
- Designed a detection logic that combined multiple log fields and temporal relationships to distinguish true events from noise.
- Produced a specification and reference implementation that could be integrated into the production back-end.
Technical Approach
Collected and labeled a large sample of logs containing fired events to be used on
Parsed each log stream to extract key patterns (sequences, timing gaps, state transitions, value thresholds).
Evaluated different pattern rules and heuristics by measuring how often they trigger and what percentage of triggers correspond to true events, iterating until the logic achieved ≥99% accuracy to fired event.
Implemented the finalized detection algorithm in Python as a clear, testable reference.
Technical diagram or schematic would be displayed here
Results
Project results visualization
Delivered a robust detection logic with ≥99% accuracy (percentile-based analysis) against historical data, enabling reliable event recognition from raw logs.
The Python reference implementation was later ported to Node.js by other developers and deployed on a server as part of the production pipeline.
(Models and drawings are omitted due to proprietary constraints)