Knowledge Graph Data Virtualization for Enterprise Silos
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Solution Overview
Problem
Existing data storage solutions in enterprise organizations operate as isolated silos, making data less accessible across units and requiring excessive computing resources to aggregate and query large volumes of information without contextual or relational data.
Innovation Solution
A knowledge graph-based system that structures data with semantic meaning using a graph presentation model, incorporating data virtualization techniques to decouple application logic from storage systems and abstract multiple data sources, reducing the cost of knowledge graph construction and improving data accessibility and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in separate data stores managed by different units, then data security and management control are improved, but data accessibility and sharing efficiency deteriorate
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between separate data stores. The knowledge graph contains schema mappings that link data elements across different data stores without requiring direct access to the underlying stores. This mediator enables cross-unit data querying and sharing while preserving the security and management control of individual data stores, as the knowledge graph abstracts and controls access to the distributed data sources.
2Quantity of substance
If large volumes of data are aggregated without contextual or relational information, then data volume and coverage are improved, but computing resource expenditure and query efficiency deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing schema mappings, data element relationships, and contextual information in the knowledge graph before queries are executed. When queries are received, the system leverages these pre-established relationships to efficiently route queries to relevant data stores and retrieve only necessary data, rather than scanning or aggregating entire data volumes. This preliminary structuring of data relationships significantly reduces computing resource expenditure during query execution.
3Ease of operation
If data is aggregated into large data lakes or warehouses, then data centralization and accessibility are improved, but data freshness and security control deteriorate
Solution Approach 1:
The knowledge graph serves as an intermediary that maintains real-time or near-real-time mappings to source data stores without requiring data duplication. Instead of aggregating data into a static data lake, the knowledge graph dynamically references the latest data from source systems through its schema mappings. This approach preserves data freshness by continuously reflecting changes in source systems while maintaining centralized accessibility through the knowledge graph interface.
Data Source
AI summary
A knowledge graph virtualization system (KGVS) implements data virtualization techniques using semantic knowledge when parsing a knowledge graph to return results to a query. The data virtualization using semantic knowledge enable end users and applications to describe desired output without detailed knowledge of underlying data storage, schema, and relationships.