The propagation of Internet-of-Things (IoT) sensors aboard moving objects has led to continuous spatiotemporal data streams that demand on-device, low-latency analysis. However, the underlying systems for processing this streaming data are ill-prepared. On the one hand, common stream processing engines lack support for spatiotemporal operations. On the other hand, existing libraries for spatiotemporal data are optimized for historical data rather than real-time processing. To bridge this gap, we present MobilityNebula, an integration between MEOS (Mobility Engine Open Source), a lightweight C library for spatiotemporal data management, and NebulaStream, a stream processing system designed for the edge-fog-cloud continuum. We evaluated MobilityNebula by deploying the system on edge devices, ingesting data from the Belgian railway operator (SNCB) trains, and performing real-time geospatial processing for point-based queries.

Engineering high-performance query execution engines is a challenging task. Query compilation provides excellent performance, but at the same time introduces significant system complexity, as it makes the engine hard to build, debug, and maintain. To overcome this complexity, we propose Nautilus, a framework that combines the ease of use of query interpretation and the performance of query compilation. On the one hand, Nautilus provides an interpretation-based operator interface that enables engineers to implement operators using imperative C++ code to ensure a familiar developer experience. On the other hand, Nautilus mitigates the performance drawbacks of interpretation by introducing a novel trace-based, multi-backend JIT compiler that translates operators into efficient code. As a result, Nautilus bridges the gap between compilation and interpretation and provides the best of both worlds, achieving high performance without sacrificing the productivity of engineers.