apache/iotdb
Apr 2026 – Apr 2026
1 Month active
Languages Used
Java
Technical Skills
JavaSQLdata analysismachine learning
Yunxiang Su
PROFILE
Yunxiang Su
During April 2026, this developer contributed to the apache/iotdb repository by designing and implementing three user-defined functions for time series data processing: Percentile, Quantile, and Cluster. Leveraging Java and SQL, they introduced percentile and quantile aggregations alongside clustering algorithms such as k-means, k-shape, and medoid, expanding IoTDB’s analytics capabilities. Their work included thorough test-driven development and full integration of these UDFs into the existing library framework, ensuring maintainability and reliability. By enabling advanced analytics and pattern discovery directly within IoTDB, this contribution supports improved decision support, anomaly detection, and segmentation for time-series datasets in production environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,245
Activity Months1
Your Network
33 peopleShared Repositories
33
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 — Key delivery: Time Series Data Processing UDFs: Percentile, Quantile, and Cluster for apache/iotdb. These UDFs introduce percentile and quantile aggregations and clustering capabilities (k-means, k-shape, medoid) for time series data, expanding IoTDB's analytics toolkit and enabling more advanced data insights. The work included new tests and full integration into the IoTDB library framework, ensuring reliability and maintainability. Impact: Empowers data scientists and developers to perform percentile/quantile-based analytics and cluster-based pattern discovery directly within IoTDB, improving decision support, anomaly detection, and segmentation for time-series datasets. Technologies/skills demonstrated: UDF framework, Java/IoTDB development, algorithm implementation (k-means, k-shape, medoid), test-driven development, integration testing, code review discipline.
1 Commits • 1 Features
Apr 1, 2026April 2026 — Key delivery: Time Series Data Processing UDFs: Percentile, Quantile, and Cluster for apache/iotdb. These UDFs introduce percentile and quantile aggregations and clustering capabilities (k-means, k-shape, medoid) for time series data, expanding IoTDB's analytics toolkit and enabling more advanced data insights. The work included new tests and full integration into the IoTDB library framework, ensuring reliability and maintainability. Impact: Empowers data scientists and developers to perform percentile/quantile-based analytics and cluster-based pattern discovery directly within IoTDB, improving decision support, anomaly detection, and segmentation for time-series datasets. Technologies/skills demonstrated: UDF framework, Java/IoTDB development, algorithm implementation (k-means, k-shape, medoid), test-driven development, integration testing, code review discipline.
April 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
JavaSQLdata analysismachine learning
Repositories Contributed To
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