Clustering Model Management via Metadata Tables
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In big data environments, data engineers face challenges in obtaining and managing optimal clustering models, as hundreds or thousands of models are generated, making it difficult to reduce and track them for reference or reuse.
Innovation Solution
A method is introduced that utilizes a large data electronic file system to persist and manage clustering analysis by generating unique identifiers for job, configuration, and data regularization elements, along with meta-data, allowing for tracking and reuse of clustering model analysis within a platform that selects data features, generates cluster models using multiple algorithms, and recommends an optimal model for use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple clustering algorithms are used to generate comprehensive clustering models, then the quality and accuracy of the clustering analysis is improved, but the number of generated models increases to hundreds or thousands making management difficult
Solution Approach 1:
The patent segments the large set of clustering models into organized groups using management tables that categorize models by algorithm type, data set, and other parameters. This segmentation allows the system to handle hundreds or thousands of models systematically rather than as an unmanageable bulk, resolving the contradiction between comprehensive model generation and manageable model organization.
Solution Approach 2:
The patent introduces management tables as an intermediary layer between the clustering algorithms and the final analysis. These tables store metadata about each model including algorithm type, data set used, and performance metrics, serving as a mediator that organizes and summarizes the large number of generated models, making them tractable for further analysis without losing the benefits of comprehensive model generation.
2Reliability
If hundreds or thousands of clustering models are generated to ensure optimal model selection, then the reliability of the clustering analysis is improved, but the time and resources required to track and evaluate models increases significantly
Solution Approach 1:
The patent creates simplified representations or copies of the full model information in the management tables. Instead of directly managing and tracking every detail of hundreds or thousands of complex clustering models, the system maintains metadata copies that capture essential information such as algorithm type, data set, and key performance indicators. This copying approach preserves the reliability benefits of comprehensive model generation while dramatically reducing the time required to track and evaluate models.
3Manufacturing precision
If comprehensive clustering analysis is performed on all generated models, then the quality of the optimal model selection is improved, but the computational resources and processing time required increases excessively
Solution Approach 1:
The patent extracts only the essential metadata and key performance indicators from the full clustering models and stores them in management tables. This extraction allows the system to perform preliminary evaluation and filtering based on the extracted information, identifying a smaller subset of promising models for more detailed analysis. This approach maintains high precision in optimal model selection while improving processing efficiency by avoiding exhaustive analysis of all model details.
Data Source
AI summary
A method of persisting and performing a clustering analysis through use of a large data electronic file system includes generating a job identifier and linking the job identifier with a configuration identifier, a plurality of model identifiers and a plurality of data regularization identifiers. Each of the configuration identifier, model identifiers and data regularization identifiers are stored in respective management tables of the file system along with meta-data indicating a physical location of an analysis configuration, a physical location of a data regularizer and a physical location of a clustering model, respectively. The method further includes specifying the job identifier to a clustering analysis application causing the analysis configuration, the clustering models and the data regularizers to load into the clustering analysis application and receiving a plurality of scores resulting from a cluster analysis performed by the clustering analysis application based on the job identifier.