Geospatial Map Augmentation With Ontology-Mapped Time Series
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Solution Overview
Problem
Existing geospatial representations lack integration and synchronization of different data types and formats, particularly time series data, limiting analysis and visualization capabilities.
Innovation Solution
A system and method that integrates and synchronizes data types and formats by using an ontology to map and visualize data, including time series data, on a geospatial representation, enhancing analysis and visualization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing geospatial representations are used, then the system maintains simplicity and existing functionality, but the analysis and visualization capabilities are limited and cannot fully leverage time series data
Solution Approach 1:
The patent introduces an ontology as an intermediary layer that maps and integrates time series data with geospatial representations. This ontology acts as a mediator between the existing geospatial system and new time series data, enabling enhanced analysis capabilities without completely redesigning the core system architecture.
Solution Approach 2:
The system is enhanced to handle multiple data types (geospatial data, time series data, event data) through a unified ontology framework. This multi-functional approach allows the same system to process and visualize diverse data formats, improving versatility while maintaining a cohesive architecture.
2Loss of information
If time series data and event data are integrated into geospatial representations, then comprehensive analysis capabilities are achieved, but data synchronization and integration complexity increase
Solution Approach 1:
The ontology transforms diverse data types into a unified parameter structure with standardized fields for temporal, spatial, and event attributes. By changing the parameter representation of time series and event data to match geospatial data structures, the system achieves complete data integration while simplifying synchronization through consistent parameter handling.
3Adaptability or versatility
If multiple data types are synchronized within the geospatial representation, then the visualization and analysis capabilities are enhanced, but the system operation complexity increases
Solution Approach 1:
The system segments the integration process into distinct layers: the ontology layer for data mapping, the processing layer for synchronization, and the visualization layer for display. This segmentation allows each layer to handle specific tasks independently, improving ease of operation by providing clear separation of concerns while maintaining comprehensive multi-data-type integration.
Data Source
AI summary
Provided herein are methods for obtaining or generating a map representation, receiving a query pertaining to a location within the map representation, in response to receiving the query, obtaining event data at the location from an external source, obtaining sensor data from an external sensor, the sensor data including time series data and being mapped to the event data according to an ontology, based on a geospatial or temporal relationship between the event data and the sensor data, and augmenting the map representation with the event data and the sensor data.