Efficient Management of High-Frequency Sensor Data Streams Using a Read-Optimized Learned Index.

The rapid growth of sensor data in IoT and Digital Twins necessitates high-performance spatial indexing. Traditional indexes like Rtrees suffer from high storage overhead, while state-of-the-art learned indexes like GLIN encounter a "Refinement Bottleneck" due to coarse-grained Minimum Bounding Rectangle (MBR) filtering. Furthermore, existing solutions often trade update throughput for query accuracy, failing in dynamic IoT workloads with concurrent reads and writes. We propose DyGLIN (Dynamic Generate Learning-Based Index), a dynamic, read-optimized learned spatial index tailored for high-frequency sensor streams. DyGLIN introduces a decoupled leaf architecture separating query processing from data maintenance. To accelerate queries, we implement a hierarchical filtering pipeline using hierarchical MBRs (HMBR) and Cuckoo Filters to aggressively prune false positives. For maintenance, a Delta Buffer mechanism amortizes update costs, while logical deletion ensures high throughput. Experiments on real-world datasets show that DyGLIN reduces query latency by 26.4% [95% CI: 20.1%, 38.6%] compared to GLIN. It achieves 30.0% [95% CI: 21.4%, 35.9%] higher insertion throughput and superior deletion performance, with only an 18.5% [95% CI: 16.8%, 19.8%] increase in memory overhead.