NLP Tiered Clustering for Automated Resource Selection
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Solution Overview
Problem
Poorly planned resource selection in large organizations leads to inefficiencies due to ad hoc methods, resulting in resource acquisition from inefficient sources and deaggregation of related identifiers, making centralized resource management challenging with diverse resources and changing efficiencies.
Innovation Solution
A system utilizing natural language processing based tiered clustering to preprocess identifiers into vectors, cluster them, and perform a second-level clustering based on string edit distance to determine a final identifier associated with the most optimal resource, thereby automating the resource selection process without manual curation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized resource acquisition guidance is implemented, then resource selection efficiency is improved, but system complexity and management difficulty increase due to diverse resources and changing efficiencies
Solution Approach 1:
The system enables resources to self-categorize and self-organize through automated NLP processing of identifiers. Resources are automatically tagged with metadata and grouped into clusters based on their characteristics, eliminating the need for manual centralized classification while maintaining efficient resource selection through self-organizing clusters.
Solution Approach 2:
Manual centralized resource management is replaced with an automated computational system using natural language processing and machine learning algorithms. The system automatically processes resource identifiers, extracts features, performs clustering, and selects optimal resources without human intervention, substituting mechanical manual processes with automated computational processes.
2Adaptability or versatility
If ad hoc resource selection is performed, then flexibility is maintained, but resource acquisition efficiency deteriorates and related identifiers become deaggregated
Solution Approach 1:
The system performs preliminary processing of resource identifiers by extracting features, generating metadata tags, and pre-organizing resources into clusters before actual resource selection is needed. This preliminary organization maintains flexibility for various selection scenarios while significantly improving acquisition efficiency by having resources pre-categorized and ready for rapid retrieval.
Solution Approach 2:
The system introduces an intermediary layer of automated NLP processing and clustering algorithms between the resource pool and the selection process. This intermediary automatically aggregates related identifiers through feature extraction and clustering, maintaining flexibility while improving efficiency by preventing deaggregation of related resources.
3Measurement precision
If manual curation of resource identifiers is performed, then accuracy is improved, but time consumption and operational overhead increase
Solution Approach 1:
Manual curation of resource identifiers is completely replaced with automated natural language processing and machine learning-based clustering algorithms. The system automatically extracts features from identifiers, generates metadata tags, performs hierarchical clustering, and selects representative identifiers without any human intervention, achieving high accuracy while eliminating manual processing time.
Solution Approach 2:
The system enables resource identifiers to self-categorize and self-organize through automated feature extraction and clustering algorithms. Each identifier is automatically processed to extract its characteristics, tagged with relevant metadata, and grouped into appropriate clusters based on similarity metrics, eliminating the need for manual classification while maintaining high accuracy.
4Loss of information
If comprehensive resource tracking is implemented, then resource aggregation is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The system segments the comprehensive resource tracking process into distinct hierarchical stages: feature extraction from identifiers, metadata tag generation, initial clustering based on features, and representative identifier selection. This segmentation divides the complex computational task into manageable stages, improving resource aggregation while controlling computational complexity through progressive processing.
Solution Approach 2:
The system applies different processing strategies to different aspects of resource identifiers based on their local characteristics. Feature extraction focuses on specific linguistic patterns, clustering applies similarity metrics tailored to the data, and representative selection uses criteria optimized for each cluster's properties. This localized approach improves aggregation quality while managing computational complexity by applying appropriate processing to each aspect.
Data Source
AI summary
A system comprises an interface configured to receive an identifier, a processor configured to determine a grouping associated with the identifier, wherein the grouping is determined using a first clustering, wherein the first clustering is based at least in part on a language processing system, determine a sub-grouping of the grouping associated with the identifier, wherein the sub-grouping is determined using a second clustering, determine a final identifier based at least in part on the identifier and the sub-grouping, determine a resource based at least in part on the final identifier, and store the final identifier associated with the resource, and a memory coupled to the processor and configured to provide the processor with instructions.