Autotransform System for Data Segmentation and Regression Modeling
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Solution Overview
Problem
As data storage grows, existing technologies face challenges in quickly and accurately analyzing and communicating large datasets, making it difficult to model and visualize relationships within the data.
Innovation Solution
An apparatus that groups datapoints into bins based on identifying ranges and calculates medians and performance values using a regression analysis, presenting an illustration of the identifying ranges and associated medians when the performance value exceeds a baseline, facilitating faster and more accurate data modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data storage grows, then data capacity increases, but data analysis and communication become more difficult and tedious
Solution Approach 1:
The patent segments data into groups based on identifying ranges, calculating medians for each group and performing regression analysis to create simplified models. This segmentation transforms complex datasets into manageable groups with representative values, making analysis more efficient despite increased data capacity.
Solution Approach 2:
The patent extracts key characteristics from data by calculating medians of groups and deriving performance values through regression analysis. This extraction creates simplified representations (models) that capture essential data relationships without requiring analysis of every individual data point, thereby reducing the tediousness of data communication.
2Quantity of substance
If data storage grows, then data capacity increases, but modeling accuracy becomes more difficult to achieve
Solution Approach 1:
By dividing data into groups based on identifying ranges and calculating medians for each group, the patent creates simplified models that maintain accuracy despite large data capacity. The segmentation allows the model to capture patterns without being overwhelmed by the volume of data.
Solution Approach 2:
The patent transforms data parameters by replacing individual data points with group medians and using regression analysis to create performance values. This parameter transformation simplifies the data representation while preserving modeling accuracy, enabling accurate models even as data capacity increases.
Data Source
AI summary
According to one embodiment, an apparatus stores a plurality of datapoints. A datapoint comprises a first value and a second value that depends upon the value of the first value. The apparatus associates the datapoint with a group from a plurality of groups. The group is associated with an identifying range and the datapoint is associated with the group based at least in part upon the first value of the datapoint and the identifying range of the group. The apparatus calculates a median of the second values of the datapoints associated with the group and a performance value by performing a regression based at least in part upon the identifying range and the calculated median of the group. The apparatus determines that the performance value exceeds a baseline value and in response, presents, on a display, an illustration depicting the identifying range and the associated median of the group.