Mobility Stream Processing on NebulaStream and MEOS

2025-11-25

AI Summary

Existing stream processing systems lack real-time spatiotemporal analytics capabilities, while mainstream spatiotemporal libraries are primarily designed for batch processing of historical data and thus ill-suited for the massive, high-velocity spatiotemporal streams generated by IoT mobile sensors. To address this gap, we propose a novel architecture integrating the MEOS spatiotemporal processing engine with the NebulaStream streaming data management system—the first deep integration of MEOS into a stream processing platform. Our approach enables real-time geofencing and complex spatiotemporal event detection for dynamic moving objects at the edge. Leveraging MEOS’s spatiotemporal indexing, moving object modeling, and event processing primitives, the system processes live telemetry streams from SNCB train-edge devices. In real-world transportation scenarios, it achieves low-latency (<500 ms), high-accuracy visualization of operational states, environmental impact monitoring, and spatiotemporal pattern recognition—thereby bridging a critical technological gap in real-time spatiotemporal analytics.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Processing real-time spatiotemporal streaming data from IoT sensors
• Integrating spatiotemporal capabilities into IoT stream processing systems
• Analyzing live mobility data from moving objects like trains

Innovation

• Integrating MEOS library with NebulaStream system
• Processing spatiotemporal streaming data in real-time
• Enabling geofencing and complex event processing