DataBytes-Organisation/Intelligent-IoT-Data-Management
Apr 2026 – May 2026
2 Months active
Languages Used
JavaScriptPython
Technical Skills
DjangoIoTNode.jsdata analysisfull stack developmentmachine learning
kimheangsrorn
PROFILE
Kimheangsrorn
Developed and integrated advanced anomaly detection features for the Intelligent-IoT-Data-Management repository, focusing on improving the reliability of IoT time-series data. Delivered both LevelShiftAD and LOF-based detectors, wiring them into the existing data processing pipeline using Python and Django. Refactored the LevelShiftADDetector to provide a unified output structure, supporting easier downstream analytics and consistent anomaly reporting. Enhanced system robustness by adding edge-case handling for small datasets in the LOFDetector. The work combined skills in machine learning, data analysis, and full stack development, resulting in a more proactive, scalable, and maintainable approach to anomaly detection within IoT data streams.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
3
Lines of code
216,072
Activity Months2
Your Network
25 peopleShared Repositories
25
Work History
May 2026
3 Commits • 2 Features
May 1, 2026May 2026: Implemented core anomaly detection enhancements for Intelligent-IoT-Data-Management. Delivered LOF-based anomaly detection integrated into the data processing pipeline, refactored LevelShiftADDetector for a unified output contract, and added a guard to LOFDetector to gracefully handle small datasets. These changes improve detection accuracy, output consistency, and system robustness, enabling more reliable IoT data monitoring and easier integration with downstream analytics.
3 Commits • 2 Features
May 1, 2026May 2026: Implemented core anomaly detection enhancements for Intelligent-IoT-Data-Management. Delivered LOF-based anomaly detection integrated into the data processing pipeline, refactored LevelShiftADDetector for a unified output contract, and added a guard to LOFDetector to gracefully handle small datasets. These changes improve detection accuracy, output consistency, and system robustness, enabling more reliable IoT data monitoring and easier integration with downstream analytics.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026In April 2026, delivered LevelShiftAD anomaly detection for Intelligent IoT Data Management. Implemented the detector and wired it into the existing time-series data processing pipeline, enabling proactive anomaly detection on IoT device data. This work enhances data quality and reliability, supports earlier incident response, and sets the stage for future alerting and automation. The contribution demonstrates solid integration skills, clear commit traceability, and a foundation for scalable anomaly detection in the data platform.
April 2026
1 Commits • 1 Features
Apr 1, 2026In April 2026, delivered LevelShiftAD anomaly detection for Intelligent IoT Data Management. Implemented the detector and wired it into the existing time-series data processing pipeline, enabling proactive anomaly detection on IoT device data. This work enhances data quality and reliability, supports earlier incident response, and sets the stage for future alerting and automation. The contribution demonstrates solid integration skills, clear commit traceability, and a foundation for scalable anomaly detection in the data platform.
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
JavaScriptPython
Technical Skills
DjangoIoTNode.jsPythonPython programminganomaly detectiondata analysisdata sciencefull stack developmentmachine learning
Repositories Contributed To
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