Data Cloud Servers Stratifying Samples for Estimation
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Solution Overview
Problem
Researchers and stakeholders face challenges in accurately estimating data with limited underlying data, requiring innovative methods to improve data analysis operations.
Innovation Solution
A method and system utilizing data cloud servers that involve receiving requests, identifying data sources, forming queries, retrieving and stratifying data sets, computing projection factors, and selecting samples to perform data estimation using multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources and advanced processing techniques are used to improve data estimation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the data estimation process into multiple distinct stages: data collection from diverse sources, data preprocessing and cleaning, stratification of data into segments, application of projection factors to extrapolate from samples to populations, and final estimation computation. This segmentation allows complex operations to be managed through modular, sequential processing steps, resolving the contradiction by organizing complexity into manageable segments that collectively improve measurement precision.
Solution Approach 2:
The patent introduces projection factors as an intermediary computational element that bridges the gap between limited sample data and population-level estimates. These projection factors serve as mediators that transform raw sample statistics into scaled population estimates, enabling accurate measurement without requiring complete population data. This intermediary mechanism resolves the contradiction by providing a mathematical bridge that improves precision while avoiding the need for exhaustive data collection systems.
2Measurement precision
If multiple data sources are queried and processed, then data estimation accuracy is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing projection factors based on historical data and population characteristics before actual estimation requests are made. When estimation is needed, the system retrieves pre-computed factors and applies them to current sample data, avoiding the need to perform complex extrapolation calculations from scratch. This preliminary preparation resolves the contradiction by shifting computational burden to advance timing, reducing real-time processing delays while maintaining accuracy.
Solution Approach 2:
The patent segments data processing into parallel independent operations: multiple data sources are queried simultaneously, data cleaning and stratification occur in parallel pipelines, and projection factor application is performed independently for each data segment. This segmentation enables concurrent processing that reduces total execution time while maintaining comprehensive data analysis, resolving the contradiction by distributing time-consuming operations across multiple parallel pathways.
Data Source
AI summary
Systems and methods are provided for managing and accessing data using one or more data cloud servers. An exemplary method includes: receiving from one or more data sources, a first data set; stratifying the first data set into first samples; receiving from second one or more data sources, a second data set; stratifying the second data set into second samples; computing a projection factor for each of the second samples using the first samples; computing projected samples using the projection factor for each of the second samples; receiving from third one or more data sources, a third data set; computing a parameter using the third data set; selecting one or more of the projected samples to form a fourth data set; and performing a computer operation for estimating the data using the fourth data set and the parameter.