Relationship Identification System for Data Entities
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Solution Overview
Problem
Existing information retrieval systems fail to effectively identify and leverage relationships among data entities organized using diverse taxonomy schemes, missing potential information inherent in these classifications.
Innovation Solution
A computer-executed relationship identification system that determines and displays relationship intimacy values for data entities across multiple folders, utilizing existing taxonomy schemes to identify both asserted and inferred relationships, enhancing data access and management efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data entities are organized in multiple folders using diverse taxonomy schemes, then data classification and organization capability is improved, but the ability to identify relationships among data entities deteriorates
Solution Approach 1:
The system performs multiple functions: it maintains diverse taxonomy schemes for organizing data entities while simultaneously computing relationship intimacy values across all folders to identify relationships. This multi-functionality resolves the contradiction by making the system both adaptable to different classification schemes and capable of relationship discovery.
Solution Approach 2:
The relationship intimacy value computation acts as an intermediary layer between the diverse taxonomy schemes and the relationship identification process. By computing intimacy values based on folder co-membership across different taxonomies, the system mediates between multiple classification schemes to reveal relationships that would otherwise be lost.
2Measurement precision
If relationship intimacy values are computed across all data entities, then relationship identification accuracy is improved, but computational complexity and processing time worsen
Solution Approach 1:
The system segments the computational process into discrete steps: (1) identifying folders containing data entities, (2) computing relationship intimacy values based on folder co-membership, and (3) generating relationship identifications. This segmentation makes the complex computation more manageable and efficient.
Solution Approach 2:
The system computes relationship intimacy values for data entities based on their co-membership in folders, which may be more computations than strictly necessary (excessive action), but this ensures comprehensive relationship identification accuracy. The approach prioritizes accuracy over minimal computation.
3Productivity
If relationship identification leverages existing taxonomy structures, then data access efficiency is improved, but system complexity increases
Solution Approach 1:
The system leverages existing taxonomy structures and folder organizations that are already in place, allowing the system to serve itself by using the existing organizational framework rather than requiring a complete redesign of the data structure. This self-service approach improves efficiency without adding significant complexity.
Data Source
AI summary
According to one embodiment, a computer-executed system includes a relationship identification tool coupled to one or more data storage systems and a user interface. Each data storage system has multiple data entities that are organized in at least one folder according to a particular taxonomy scheme. The relationship identification tool is operable to receive data entities from the data storage systems, and determine relationship intimacy values for each data entity relative to the other plurality of data entities according to its taxonomy scheme. These relationship intimacy values are then displayed on the user interface.