2025/11/25 by Duarte, Mariana M. Garcez, Nugroho, Dwi P. A., Tod, Georges +7
Computer Science · Engineering · #Advanced Database Systems and Queries #Analytics #Data Management and Algorithms #Data processing #Databases (cs.DB) #Enhanced Data Rates for GSM Evolution #FOS: Computer and information sciences #Focus (optics) #Geospatial analysis #Process (computing) #Scalability #Stream processing #Traffic Prediction and Management Techniques #Train
paper · open access · doi:10.48550/arxiv.2511.20084
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/25 · openalex created_date 2025/11/28 · openalex updated_date 2026/07/28
The increasing use of Internet-of-Things (IoT) sensors in moving objects has resulted in vast amounts of spatiotemporal streaming data. To analyze this data in situ, real-time spatiotemporal processing is needed. However, current stream processing systems designed for IoT environments often lack spatiotemporal processing capabilities, and existing spatiotemporal libraries primarily focus on analyzing historical data. This gap makes performing real-time spatiotemporal analytics challenging. In this demonstration, we present NebulaMEOS, which combines MEOS (Mobility Engine Open Source), a spatiotemporal processing library, with NebulaStream, a scalable data management system for IoT applications. By integrating MEOS into NebulaStream, NebulaMEOS utilizes spatiotemporal functionalities to process and analyze streaming data in real-time. We demonstrate NebulaMEOS by querying data streamed from edge devices on trains by the Société Nationale des Chemins de fer Belges (SNCB). Visitors can experience demonstrations of geofencing and geospatial complex event processing, visualizing real-time train operations and environmental impacts.