Cross-model filtering via relationship ontology
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Solution Overview
Problem
In a distributed computing environment, it is challenging to interrelate and display data from different sources controlled by different entities, as existing technologies lack efficient mechanisms to correlate and present data across separate data models.
Innovation Solution
A method is implemented that performs queries on multiple data sets, uses a relationship ontology to identify relationships between data from different sources, and applies filters or highlights based on these relationships, allowing for cross-model filtering and visualization of correlated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is obtained from different sources under control of different entities, then data diversity and source independence are improved, but data interrelation and correlation capability deteriorate
Solution Approach 1:
The patent introduces an intermediary layer (data integration system with ontology) that mediates between multiple independent data sources and the user. This intermediary automatically discovers, models, and manages relationships between data from different entities, enabling correlation without requiring direct integration between source systems, thus maintaining source independence while achieving data interrelation.
Solution Approach 2:
The patent segments the data integration problem into distinct layers: data source layer (independent entities), integration layer (ontology and relationship management), and presentation layer (user interface). This segmentation allows each layer to operate independently while maintaining defined interfaces, resolving the contradiction between source independence and integration capability.
2Device complexity
If data from multiple sources is gathered into a single database, then data interrelation capability is improved, but system complexity and centralization requirements worsen
Solution Approach 1:
The patent creates a universal data integration system that can work with multiple different data sources and types without requiring each source to conform to a single centralized schema. The ontology-based approach provides multi-functionality by adapting to various data models while maintaining consistent relationship management, thus achieving integration capability without forcing centralization.
Solution Approach 2:
Rather than gathering all data into a single centralized database, the patent uses an intermediary ontology layer that sits between distributed data sources and users. This intermediary enables data interrelation by modeling relationships across sources without requiring physical centralization, preserving distributed system flexibility while achieving integration capability.
3Ease of operation
If traditional database operations are used for single-source data, then data manipulation simplicity is improved, but cross-source data correlation capability worsens
Solution Approach 1:
The patent adds another dimension to traditional database operations by introducing the ontology layer that models relationships between data from different sources. This additional dimension enables cross-source correlation while preserving the simplicity of traditional operations within each source, as users can work with familiar data models locally while the ontology handles cross-source relationships in the extended dimension.
Data Source
AI summary
Presenting data from different data providers in a correlated fashion. A first query is performed on a first data set controlled by a first entity to capture a first set of data results. Then a second query is performed on a second data set controlled by a second entity to capture a second set of data results. A relationship ontology that correlates data stored in different data stores controlled by different entities is then consulted to identify one or more relationships between data in the selected results set and the second data set.