Graph-Based Ontological Database Analysis with Version Control
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Solution Overview
Problem
Existing database analytic tools are inefficient, costly, and require substantial configuration and training, making it difficult for businesses to access and analyze large volumes of data stored in complex database systems.
Innovation Solution
A low-latency database analysis system that uses a graph-based ontological data structure with versioned nodes and edges, allowing for efficient data analysis and presentation, and includes a transaction log for reverting changes and maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database analytic tools are used, then data analysis capability is provided, but efficiency is low and cost is high
Solution Approach 1:
The patent segments the database analysis system into distinct modular components: graph database engine, ontology engine, natural language processing module, and version control system. Each module handles specific tasks independently, improving efficiency while reducing overall system complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary graph-based ontological data structure that sits between the traditional database and the analysis tools. This intermediary layer transforms complex database queries into simplified graph traversals, significantly improving analysis efficiency while shielding users from underlying system complexity.
2Ease of operation
If traditional database tools are used, then data access is enabled, but substantial configuration and training are required
Solution Approach 1:
The patent implements self-service capabilities through automatic ontology generation from database schemas and automated query optimization. The system automatically configures itself based on the data structure, eliminating the need for manual configuration and reducing training requirements for users.
Solution Approach 2:
The natural language processing intermediary translates user-friendly queries into optimized database operations automatically. This mediator handles the complexity of query formulation and optimization, allowing users to access data without learning complex query languages or requiring extensive training.
3Productivity
If graph-based ontological data structure is implemented, then data analysis efficiency is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal graph-based ontological data structure that serves multiple functions: data storage, data modeling, query optimization, and version control. This multi-functional approach improves analysis efficiency across different operations while managing complexity through a single unified structure rather than multiple specialized systems.
Solution Approach 2:
The patent implements version control by maintaining copies of the graph-based ontological data structure at different points in time. This copying mechanism enables efficient rollback and comparison operations without requiring complex real-time synchronization, improving analysis efficiency while managing versioning complexity through simple replication.
4Reliability
If version control with transaction log is implemented, then data integrity is maintained, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential version information into a separate transaction log, storing only the necessary metadata (version identifiers, timestamps, change summaries) rather than complete data copies. This extraction approach maintains data integrity through version tracking while minimizing storage requirements by keeping the log compact and selective.
Solution Approach 2:
The patent implements a discard and recover mechanism where intermediate version states are discarded in favor of maintaining only critical checkpoint versions in the transaction log. Full data can be recovered from these checkpoints combined with change logs, maintaining data integrity while reducing storage requirements by eliminating redundant intermediate states.
Data Source
AI summary
Improved systems and methods for database analysis are described herein. A method includes generating a graph-based ontological data structure including nodes connected by edges in a low-latency database analysis system, wherein each node represents a respective analytical-object in the low-latency database analysis system, maintaining versions for each of the nodes in the graph-based ontological data structure, maintaining versions for each of the edges in the graph-based ontological data structure, maintaining a transaction log for each transaction with respect to the graph-based ontological data structure, reverting to an earlier version of at least a portion of the graph-based ontological data structure using the transaction log, versioned nodes, and versioned edges in response to an event, and outputting a version of the graph-based ontological data structure in a defined form for presentation to a user or for use by a client.