Dataset Prioritization Using Clustering and Classification Axes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data analysis systems struggle to efficiently cluster and classify large datasets, leading to challenges in prioritizing and effectively displaying relevant information, which hampers decision-making processes, especially in dynamic environments.
Innovation Solution
A system and method utilizing clustering and classification modules to generate a prioritized array of datasets by applying them across derived axes, generating a priority score, and ranking the array based on this score for enhanced display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analysis systems are used to process large datasets, then data storage and basic processing can be performed, but the system cannot efficiently cluster and classify data, leading to poor prioritization and display effectiveness
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: a clustering module that groups datasets based on similarities, a classification module that categorizes clustered data, and a prioritization module that ranks datasets. This segmentation allows each module to specialize in specific tasks, improving overall processing efficiency while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system performs preliminary clustering and classification actions before final prioritization and display. By pre-processing datasets through clustering and classification stages, the system prepares data in advance for efficient prioritization, reducing the computational burden during the final ranking and display phases
2Ease of operation
If all datasets are displayed without prioritization, then complete information is available, but user interaction and decision-making are hampered by information overload
Solution Approach 1:
The patent applies local quality by providing different levels of data presentation: a prioritized view that highlights the most relevant datasets for quick decision-making, and access to the complete dataset array for users who need comprehensive information. This allows the system to optimize for ease of operation in the primary view while preserving information completeness through on-demand access to full data
Solution Approach 2:
The system adds a prioritization dimension to the data display, organizing datasets along multiple axes including relevance, recency, and importance scores. This multi-dimensional organization allows users to navigate through prioritized rankings while maintaining access to the complete information space, effectively resolving the contradiction between ease of operation and information completeness
3Measurement precision
If clustering and classification modules are applied to large datasets, then data prioritization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements partial action by applying clustering and classification to subsets of data that are most critical for prioritization, rather than processing every single dataset with the full analytical pipeline. The system identifies and processes key datasets through clustering and classification to achieve sufficient prioritization accuracy without the excessive time cost of complete processing
Solution Approach 2:
The system employs dynamic processing where the level of clustering and classification applied adapts based on data characteristics and user needs. For time-sensitive operations, the system may use simplified clustering; for comprehensive analysis, more rigorous classification is applied. This dynamic approach balances prioritization accuracy with processing time requirements
Data Source
AI summary
A system and method for determining a prioritized array of associated datasets. The system includes at least a processor and a memory communicatively connected to the at least a processor and contains instructions, wherein the at least a processor is configured to receive a plurality of datasets, apply a clustering module to the plurality of datasets, wherein the clustering module is configured to assign an individual dataset to an appropriate cluster, apply a classification module to the plurality of datasets, wherein the classification module is trained on cluster labels of one or more clusters and configured to predict labels for new individual datasets, and generate a prioritized array, wherein generating the prioritized array includes applying the plurality of instances of the plurality of datasets across one or more axes, wherein the one or more axes are derived from the clustering and classification of the plurality of datasets.