Modern GPS datasets are notoriously noisy: satellites drift, buildings scatter signals, and consumer devices introduce frequent errors. When working with millions of position samples from vehicles, smartphones, or IoT devices, this noise makes analysis unreliable. Routes jump, tracks zigzag, and outliers distort aggregates. The Kalman Filter is the standard technique for smoothing such data. Traditionally, it is applied outside the database in environments like Python or MATLAB. But for large-sc...