Feature Graph Community Labeling for Duplicate Feature Control
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Solution Overview
Problem
Existing feature stores face challenges with indiscriminate addition of features, leading to duplication and inefficiencies, which complicates data management and processing in telecommunication network databases.
Innovation Solution
A three-pronged approach involving feature disambiguation, ontology tracking, and fingerprinting is employed to identify and prevent duplicate features, followed by community detection to label and manage feature communities within a feature graph database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If features are added indiscriminately to the feature store, then the quantity of features increases, but duplication and data management complexity increase
Solution Approach 1:
The patent applies preliminary action by performing feature disambiguation, ontology tracking, and fingerprinting before features are added to the feature store. This pre-processing identifies duplicate features and establishes unique identifiers, preventing duplication before it occurs and simplifying subsequent data management operations
Solution Approach 2:
The patent introduces an intermediary layer consisting of ontology tracking and fingerprinting mechanisms that mediate between feature addition requests and the feature store. This intermediary validates features, checks for duplicates, and assigns unique identifiers, thereby managing complexity without restricting feature quantity growth
2Quantity of substance
If features are added without disambiguation, then the quantity of features increases, but feature duplication occurs
Solution Approach 1:
The patent implements feedback mechanisms through fingerprinting that compare new features against existing features in the store. When a potential duplicate is detected, the system provides feedback to prevent addition, ensuring feature uniqueness is maintained while allowing legitimate feature quantity growth
Solution Approach 2:
Feature disambiguation and fingerprinting are performed as preliminary actions before features are committed to the store. This pre-validation ensures that only unique features are added, maintaining reliability without requiring post-addition cleanup operations
3Ease of operation
If community detection is applied to label features, then feature discoverability improves, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the feature set into distinct communities based on ontology and relationships. This organization groups related features together, improving discoverability through structured navigation while enabling efficient querying within specific communities rather than searching the entire feature space
Data Source
AI summary
A processing system may apply a community detection process to a feature graph database to identify a plurality of communities of features, the feature graph database comprising: a plurality of objects, each associated with one of a feature or a concept, and a plurality of relationships between the plurality of objects. Next, the processing system may label a first plurality of features of the feature graph database with at least a first community label, where the first plurality of features comprises features of at least a first community of the plurality of communities. The processing system may then obtain a search associated with at least one feature of the feature graph database, where the at least one feature is a part of the at least the first plurality of features of the at least the first community, and provide the first plurality of features in response to the search.