Relationship Identification Engine for Implicit Data Correlations
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Solution Overview
Problem
Existing data processing methods struggle to effectively identify and quantify implicit relationships between diverse data elements, especially as dataset sizes increase, making it impractical to manually detect these relationships through traditional automated techniques.
Innovation Solution
A relationship identification engine and method that generates a relationship indicator by correlating attributes across different types of data elements, enabling the systematic identification of implicit relationships and their strengths within datasets, even across multiple levels of explicit relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated techniques are used to identify relationships in datasets, then the process is simpler to implement, but the ability to identify implicit relationships deteriorates as dataset sizes increase
Solution Approach 1:
The system segments the complex task of relationship identification into distinct modules: an explicit relationship identification engine that processes declared relationships, and an implicit relationship identification engine that processes attributes. This segmentation allows each module to specialize in specific types of relationship detection, improving overall accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces relationship indicators as intermediary elements that bridge explicit and implicit relationships. These indicators serve as mediators that connect data elements through correlated attributes, enabling the system to infer implicit relationships without requiring direct explicit declarations, thereby improving relationship identification capability.
2Productivity
If manual querying methods are used to identify implicit relationships, then the system complexity remains low, but the time required and error rate increase significantly
Solution Approach 1:
The system performs preliminary action by pre-computing and storing relationship indicators for all data elements based on their attributes. This pre-computation allows the implicit relationship identification engine to quickly retrieve and combine pre-calculated indicators rather than performing complex queries at runtime, significantly improving efficiency while reducing execution time.
Solution Approach 2:
The relationship identification system operates autonomously without requiring manual querying. The explicit and implicit relationship identification engines automatically process datasets, generate relationship indicators, and identify relationships based on correlated attributes, eliminating the need for manual intervention and reducing both time loss and human error.
3Quantity of substance
If only explicitly declared relationships are utilized, then the system is easier to implement and interpret, but the number of identified relationships is insufficient
Solution Approach 1:
The patent merges the functionality of explicit relationship identification and implicit relationship identification into a unified system. By combining the explicit relationship identification engine with the implicit relationship identification engine, the system leverages both declared relationships and attribute-based correlations, significantly increasing the total number of relationships identified while managing complexity through integrated architecture.
Solution Approach 2:
The relationship identification system achieves multi-functionality by handling both explicit relationships (through the explicit relationship identification engine) and implicit relationships (through the implicit relationship identification engine using attribute correlation). This universal approach allows the same system to serve multiple relationship detection needs, increasing the quantity of relationships identified without requiring separate specialized systems.
Data Source
AI summary
Methods, systems and articles of manufacture for discovering relationships among data elements within a dataset are disclosed. A first relationship is identified between a first data element and a second data element by identifying a correlation between a first attribute of the first data element and the first attribute of a second data element. A second relationship indicator is generated that is indicative of a relationship between the first data element and the second data element based on the correlation between the first attribute of the first and second data elements. Various embodiments can identify implicit relationships across one or more levels of explicit relationships where the explicit relationships can be across different attributes. Such techniques can be employed in various types of application programs.