Knowledge Graph Virtualization for Enterprise Data 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 for data aggregation and querying, as they lack a layer of relationship data connecting information.
Innovation Solution
A knowledge graph is introduced that structures data with semantic meaning using a graph presentation model, incorporating entities, relationships, and attributes, along with data virtualization techniques to reduce the cost of construction and improve data accessibility across heterogeneous environments.
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 across units deteriorate
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer that sits between separate data stores and applications. This knowledge graph virtualizes data from multiple sources, creating a unified view without physically consolidating data. The knowledge graph acts as a mediator that enables cross-unit data access while maintaining the original data store security boundaries, thus improving data accessibility without compromising security.
2Productivity
If data aggregation is performed across multiple data stores, then comprehensive data analysis is improved, but computing resource expenditure deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-processing and structuring data into a knowledge graph with defined schemas, entities, and relationships before analysis is needed. This upfront structuring work creates a ready-to-query framework that eliminates the need for resource-intensive data aggregation and transformation during actual analysis operations, thus improving productivity while reducing computing resource expenditure.
Solution Approach 2:
The knowledge graph creates a virtual copy or representation of data from multiple sources without physically moving or duplicating the actual data. This copying approach allows comprehensive data analysis across multiple data stores while avoiding the high computing costs associated with actual data aggregation, transformation, and synchronization.
3Ease of operation
If data is centralized in a single location, then data accessibility is improved, but data security and source system control deteriorate
Solution Approach 1:
The knowledge graph serves as an intermediary that provides centralized data access functionality without actual centralization. It virtualizes data from source systems, allowing applications to access data centrally through the knowledge graph while source systems retain full control over their data. This resolves the contradiction by decoupling accessibility from physical centralization.
4Ease of operation
If data virtualization is implemented without semantic knowledge, then data accessibility is improved, but query accuracy and relevance deteriorate
Solution Approach 1:
The patent applies parameter changes by enriching the data virtualization layer with semantic knowledge including ontologies, taxonomies, and structured relationships. This transforms the knowledge graph from a simple data routing mechanism into an intelligent system that understands data meaning, context, and relationships, thereby improving query accuracy and relevance while maintaining data accessibility.
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.