Data Sample Visualization for Computational Model Accuracy
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Solution Overview
Problem
Existing computational models lack effective means to analyze data lineage and determine the influence of data quality on model performance, leading to inefficiencies and inaccuracies.
Innovation Solution
A processor set generates visualizations based on data associations to identify and remove low-quality data subsets, updating the computational model with an improved dataset and metadata to enhance model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational models use complete datasets without analysis, then all available data is processed, but model accuracy decreases due to inclusion of low-quality data
Solution Approach 1:
The system extracts and removes low-quality data subsets from the complete dataset based on visualization analysis. By identifying and taking out problematic data portions (outliers, inconsistent patterns, low-confidence samples), the model trains on purified data that improves accuracy while reducing the effective data volume that needs processing.
Solution Approach 2:
The system applies different quality assessments to different portions of the dataset rather than treating all data uniformly. By analyzing local characteristics of data subsets through visualizations, the system identifies regions of high and low quality data, allowing selective processing that improves overall model reliability.
2Measurement precision
If computational models process all data without visualization analysis, then processing is straightforward, but data quality issues and inaccuracies remain undetected
Solution Approach 1:
The system introduces visualization outputs as an intermediary layer between raw data and model training. These visualizations serve as mediators that translate complex data quality issues into interpretable graphical representations, enabling accurate quality assessment without requiring direct complex analysis of the raw data structures.
Solution Approach 2:
The system transforms data quality assessment from a hidden, abstract computational problem into a visible, spatial representation through visualizations. By projecting data characteristics into visual dimensions (graphs, plots, spatial distributions), the system enables precise quality measurement that would be difficult to achieve through traditional computational analysis alone.
3Reliability
If low-quality data is removed based on visualizations, then model accuracy improves, but data processing time increases
Solution Approach 1:
The system performs data quality assessment and removal of low-quality subsets before the main model training process. By conducting visualization-based analysis and data purification in advance, the system prepares a cleaned dataset that accelerates subsequent training iterations, reducing the overall time investment despite the added preliminary analysis step.
Solution Approach 2:
The system removes only the critical low-quality data subsets identified through visualization rather than processing or cleaning the entire dataset. By applying partial action focused on problematic regions rather than comprehensive data processing, the system achieves performance improvement with minimal time investment.
Data Source
AI summary
A computer-implemented method is provided. A processor set receives a dataset comprising a set of data and metadata associated with the set of data from a database. The processor set generates a number of outputs by inputting the set of data and the metadata to a computational model. The processor set associates the number of outputs to the dataset to generate a number of associations. The processor set creates a number of visualizations based on context from the number of associations using a graphical user interface. The processor set removes a subset of data from the set of data and a subset of metadata associated with the subset of data from the metadata based on the visualizations to generate an updated dataset. The processor set updates functions for the computational model based on the visualizations. The processor set generates updated outputs by inputting the updated dataset to the computational model.