Ontology-Based Unified Data Access for Heterogeneous Databases
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Solution Overview
Problem
As data volumes increase, conventional approaches to accessing data stored in multiple databases become labor-intensive and inefficient, with Ontologies being complex to scale and search efficiently.
Innovation Solution
The system uses an Ontology to generate code for accessing and searching databases with different schemas and query formats, employing an Ontology Reader to convert data into standardized formats and generate GraphQL APIs for unified data retrieval across various databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in multiple different databases with different schemas and query formats, then data can be accessed with specific benefits for each database type, but accessing and searching the data becomes more difficult and labor-intensive
Solution Approach 1:
The patent introduces an intermediary layer (unified query interface, ontology model, or federated query system) that sits between the user and multiple heterogeneous databases. This intermediary translates unified search queries into database-specific queries, allowing users to access data from multiple databases using a single interface without needing to understand each database's specific schema or query format.
Solution Approach 2:
The patent creates a universal data access system that can handle multiple database types through a single unified interface. The system provides multi-functional capabilities by supporting various database schemas and query formats through a common access mechanism, eliminating the need for separate access methods for each database type.
2Productivity
If the same data is stored in multiple databases to provide efficient access to multiple users, then access efficiency improves, but organizing and discovering data becomes more difficult
Solution Approach 1:
The patent creates virtual copies or representations of data across multiple databases through a unified ontology model. Instead of physically organizing and managing duplicate data copies, the system uses conceptual copies (ontology instances) that reference the actual data in various databases, reducing organizational complexity while maintaining access efficiency.
Solution Approach 2:
The patent adds an ontological dimension to data organization, creating a hierarchical classification system that sits above the physical database structure. This additional dimension provides a unified framework for organizing and discovering data across multiple databases without altering the underlying database structures or increasing operational complexity.
3Reliability
If conventional approaches are used to access data in multiple databases, then existing systems can be maintained, but the approach becomes labor-intensive and inefficient as data volume increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining data relationships, schemas, and access paths in the ontology model before actual data queries are executed. The system pre-processes and structures the ontology to establish lookup tables, relationship mappings, and query templates in advance, reducing the time required for actual data access while maintaining system reliability.
Data Source
AI summary
A system, apparatus and methods for generating database entries and tools for accessing and searching a database from an Ontology. Starting with an Ontology used to represent data and relationships between data, the system and methods described enable that data to be stored in a desired type of database and accessed using an API and search query generated from the Ontology. Embodiments provide a structure and process to implement a data access system or framework that can be used to unify and better understand information across an organization's entire set of data. Such a framework can help enable and improve the organization and discovery of knowledge, increase the value of existing data, and reduce complexity when developing next-generation applications.