Multi-table database model for flexible link analysis
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Solution Overview
Problem
Current data storage models for link analysis are inadequate, leading to inefficient searching processes and unintuitive data representations, making it difficult to effectively perform link analysis and requiring extensive deliberation for database modifications.
Innovation Solution
A multi-table database model is implemented, using separate tables for node and link taxonomies, instances, source data, and temporal data, allowing for flexible data representation and storage without requiring changes to the data model, enabling efficient link analysis by capturing domain knowledge and allowing for the addition of new data without altering the schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional relational or object models are used for data storage, then data can be stored in a structured format, but searching processes become inefficient and data representation becomes unintuitive for link analysis
Solution Approach 1:
The patent segments the data storage model into distinct components: node tables for entity data, link tables for relationship data, and separate indexing structures. This segmentation allows optimized retrieval paths for link analysis operations, where node and link data can be accessed independently and efficiently combined through indexes, resolving the contradiction between structured storage and searching efficiency.
Solution Approach 2:
The patent introduces index structures as intermediary elements between the stored data and the analysis operations. These indexes act as mediators that enable efficient searching and joining of node and link data without requiring full table scans, thus improving searching efficiency while maintaining intuitive data representation through the original node-link structure.
2Adaptability or versatility
If business databases are constructed according to data models corresponding to business processes, then the database reflects business operations accurately, but modifications to optimize for link analysis require extensive deliberation and study
Solution Approach 1:
The patent implements a dynamic data model where the schema can be extended through configuration rather than structural modification. New node and link types can be added by defining new tables and relationships in the existing framework, allowing the database to adapt to different business processes and link analysis requirements without requiring complex schema migrations or deliberation about fundamental model changes.
Solution Approach 2:
The patent creates a universal data model framework that can serve multiple purposes: it supports traditional business process storage while simultaneously enabling link analysis operations. The node-link table structure is general enough to represent various business entities and relationships, making the database versatile for both business operations and analytical queries without requiring separate specialized models.
3Productivity
If conventional data storage models are used, then implementation is straightforward, but the models fail to fully comprehend connections among entities leading to extensive data retrieval requirements
Solution Approach 1:
The patent segments connection information into dedicated link tables that explicitly store relationship data between nodes. Instead of inferring connections from normalized business data, the link tables directly capture entity relationships, enabling the system to comprehend and efficiently retrieve connection information without requiring extensive data retrieval and joining operations across multiple business tables.
Data Source
AI summary
Systems and techniques for facilitating link analysis may be implemented by creating a data structure that includes a flexible and optimal representation of data for use in performing link analysis. The data structure may be used for storing data relating to a knowledge domain, and the data may include generic nodes, node instances, generic links, and link instances. Each generic node may define properties of potential node instances. Each node instance may be based on a corresponding generic node and may represent an entity in the knowledge domain. Each generic link may define properties and/or functions of potential link instances. Each link instance may be based on a corresponding generic link and may represent a relationship between node instances. The data may be stored in tables in a database and may be analyzed by performing a link-by-link search to infer new knowledge about relationships among entities in the knowledge domain.