Demonstrating Native TimeSeries and Spatiotemporal Capabilities

Developed by:
Jan Musil, NCEE Technical Specialist
in partnership with IBM Bob AI

May 2026

1. Introduction

This application demonstrates IBM Informix's native TimeSeries and Spatiotemporal capabilities through a practical implementation. The system manages sensor data and GPS vehicle tracking, showcasing how Informix efficiently handles time-series data while providing advanced spatial analysis features.

All data was generated by IBM Bob to closely simulate real-world scenarios. The GPS vehicle tracking uses actual routes from major cities including Prague, London, Paris, New York, Tokyo, Berlin, Sydney, and Dubai, with real landmarks such as Prague Castle, the Eiffel Tower, Times Square, and the Sydney Opera House.

Figure 1: Application Dashboard

2. Informix TimeSeries Implementation

At the heart of this application is IBM Informix's native TimeSeries technology. Unlike traditional relational databases that struggle with time-stamped data, Informix provides specialized storage and query capabilities designed specifically for time-series workloads.

TimeSeries Architecture

The system uses Informix's TimeSeries containers to store sensor measurements. Each container is optimized for sequential time-based access patterns and automatically compresses data to reduce storage requirements by approximately 50%. Virtual tables provide SQL access to the TimeSeries data, making it accessible to any application.

Real-Time Performance

The application queries the database directly in real-time with no caching layer. Query response times remain under 100 milliseconds for single sensor queries and under 500 milliseconds for aggregations across all sensors.

Figure 2: Statistical Analysis with Interactive Charts

3. Spatiotemporal Analytics

Beyond basic TimeSeries capabilities, the application demonstrates Informix's spatiotemporal features for analyzing GPS vehicle data. These features combine temporal and spatial dimensions to answer questions like "which vehicles passed through this area during this time period?"

GPS Vehicle Tracking with Real Routes

The system tracks vehicles across eight major cities with real landmarks and accurate coordinates. The GPS data was generated by IBM Bob to simulate real vehicle movement patterns using actual geographic coordinates and realistic speeds.

Figure 3: GPS Vehicle Tracking with Interactive Map

Trajectory Analysis and Subtracks

Informix's spatiotemporal functions enable trajectory analysis. The system calculates total distance traveled, average speed, and identifies stops. The subtrack feature divides long GPS trajectories into manageable segments for efficient spatial queries.

Figure 4: Advanced Analytics and Velocity Analysis

4. Sensor Data Management

The application monitors ten sensors across four categories, demonstrating how Informix TimeSeries handles different types of measurements.

Environmental Monitoring: Five sensors monitor temperature and humidity in buildings and data centers, providing stable readings that change gradually over time.

Motion Detection: Two sensors track activity in warehouse zones, measuring velocity and movement patterns to understand operational flow.

GPS Fleet Tracking: Two GPS trackers follow vehicles in real-time, showing 5 times more velocity and 50 times more location variation than stationary sensors.

Figure 5: Sensor Data and Real-Time Readings

5. Interactive Web Interface

The application provides a modern web interface built with HTML5, CSS3, and JavaScript. The home dashboard shows an overview of all sensors. The statistics page presents data through interactive charts created with Chart.js. The analytics page includes an interactive map powered by Leaflet.js displaying vehicle routes across cities.

A REST API provides programmatic access to all data and functionality, enabling integration with other applications through simple HTTP requests in JSON format.

6. Technical Implementation

The application is built with Java 17 and Spring Boot 3.2.0. The Informix database uses TimeSeries columns for sensor data and spatiotemporal extensions for GPS tracking. IBM Bob generated all data to simulate realistic scenarios with appropriate noise and variation.

7. Practical Applications

Fleet Management: Track vehicles in real-time, analyze routes for efficiency, and monitor driver behavior. Calculate distances traveled and identify stops.

Facility Management: Monitor environmental conditions across multiple locations. Optimize HVAC systems and identify equipment problems before failures.

Warehouse Operations: Analyze activity patterns, reveal peak activity times, and identify bottlenecks to optimize workflows.

8. Conclusion

This application demonstrates IBM Informix's capabilities for managing time-series and spatiotemporal data. It showcases how Informix handles real-world data management challenges using real geographic coordinates from eight major cities.

The combination of native TimeSeries storage, spatiotemporal analytics, and a modern web interface creates a platform suitable for IoT sensor management, fleet tracking, facility monitoring, and similar applications requiring time-series data analysis.