Composite Entity Geolocation Using Ontology-Based Data Fusion
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Solution Overview
Problem
Existing systems fail to accurately determine the geolocation of real-world entities by associating geolocation data from multiple sensors with the corresponding objects, particularly in heterogeneous data environments.
Innovation Solution
A system and method that utilizes an ontology-based object model to identify and process geolocation data from multiple heterogeneous data sources, employing rules engines and machine learning models to determine the geolocation of both elementary and composite objects based on ontology-defined relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geolocation data from multiple heterogeneous data sources is processed to determine object locations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex geolocation determination process into distinct functional modules: an ontology module that defines object relationships, a data source identification module that selects relevant sources, a data extraction module that retrieves geolocation information, and a determination module that computes final positions. This segmentation manages complexity by organizing the multi-source processing pipeline into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent introduces an ontology-based object model as an intermediary layer between heterogeneous data sources and the geolocation determination logic. This ontology serves as a standardized interface that translates diverse data formats from different sources into a unified representation, enabling accurate location determination without directly managing the complexity of each individual data source's structure and protocols.
2Reliability
If multiple data sources are integrated to determine composite object geolocations, then reliability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The ontology-based object model provides a universal framework that can represent multiple types of objects (elementary and composite) and their relationships across different data sources. This universal representation enables the system to reliably determine geolocations for various object types using the same core logic, regardless of the heterogeneity of underlying data sources, thereby improving reliability while managing detection difficulty through standardization.
Solution Approach 2:
The system transforms heterogeneous geolocation data from different sources by changing their parameters into a unified coordinate system and temporal format. The determination module adjusts and normalizes location parameters (coordinates, timestamps, accuracy metrics) from diverse sources, enabling reliable composite object location calculation while simplifying the measurement process through parameter standardization.
3Productivity
If geolocation data is processed in real-time from multiple sources, then productivity is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary actions by pre-defining ontology relationships and object models before geolocation determination is needed. The ontology module establishes the structural framework and data source mappings in advance, so that when real-time geolocation requests occur, the system can quickly query and process data without performing complex structural analysis, thereby improving productivity while reducing real-time energy consumption.
Data Source
AI summary
An example method of determining geolocations of composite entities based on information retrieved from heterogeneous data sources comprises: identifying, by a computer system, an association of a first object and a second object with a composite object; receiving, from a first data source associated with the first object by an ontology, a first dataset including a first data item specifying a first time identifier and a first geolocation associated with the first object; receiving, from a second data source associated with the second object by the ontology, a second dataset including a second data item specifying a second time identifier and a second geolocation associated with the second object; and determining, by applying a rule set associated with the ontology to the first dataset and the second dataset, a geolocation of the composite object and a corresponding time identifier.