Repository Data Correlation via Group-Level Attribute Segmentation
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Solution Overview
Problem
Repository systems face inefficiencies in correlating data due to the need for extensive pair-wise comparisons of resources or objects, which consume significant computing resources and fail to identify meaningful relationships between groups of resources or objects.
Innovation Solution
The system determines correlations by comparing attributes of groups of objects rather than individual objects, reducing processing time and eliminating the need for pair-wise comparisons, and performs actions such as merging or updating data based on identified correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pair-wise comparison of resources or objects is performed to identify duplicate data, then duplicate data can be identified and reduced, but computing resources are significantly consumed
Solution Approach 1:
The patent segments the correlation process into two levels: group-level correlation (comparing attributes of groups of objects) and object-level correlation (comparing individual objects only when their group attributes correlate). This segmentation reduces computing resource consumption by avoiding exhaustive pair-wise comparisons of all individual objects, while maintaining duplicate identification accuracy through the hierarchical approach.
2Reliability
If pair-wise comparison of resources or objects is performed to identify duplicate data, then duplicate data can be identified, but processing time increases
Solution Approach 1:
The patent applies preliminary action by first performing group-level correlation analysis on attributes of groups of objects before conducting object-level comparisons. This preliminary filtering step identifies which groups contain potential duplicates, allowing the system to focus subsequent detailed comparisons only on relevant object pairs, thereby significantly reducing overall processing time while maintaining identification accuracy.
3Reliability
If traditional correlation strategies are used, then duplicate data identification is possible, but meaningful relationships between groups of resources or objects cannot be identified
Solution Approach 1:
The patent introduces a new dimension of analysis by implementing group-level correlation that operates above the individual object level. This dimensional escalation allows the system to identify relationships between groups of objects based on their attributes, providing both duplicate identification at the object level and meaningful relationship detection at the group level, thereby enhancing versatility without sacrificing duplicate detection capability.
Data Source
AI summary
Techniques are described for determining correlations between data in a repository system. The data may include information about corresponding to resources (e.g., an application, a process, a service, an endpoint, or a method) in a computing environment. A correlation between objects can indicate a similarity or a relationship based on one or more of the attributes of each object that is correlated. The repository system can store information about each object in a data structure, such as an entity, including the attributes about the object. The repository system can determine the relationships between entities based on correlations identified from the attributes of entities. The repository system can perform correlations based on groups of entities corresponding to a group of objects. Upon determining that two groups of entities match, the repository system can compare individual entities in the groups to identify correlations between individual entities corresponding to objects that are correlated.