Randomized Data Selection With Exact Proportionality at Scale
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing randomized selection methods fail to guarantee an exact proportion of data elements are selected, which is crucial for applications requiring precise data subset proportions, and can lead to unwanted biases and drift over time.
Innovation Solution
A system generates a randomization scheme that includes a ranking of random values and a predetermined threshold, allowing for scalable and distributed selection of data subsets that enforce the desired proportion, reducing biases and maintaining proportionality across multiple computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional randomized selection methods are used, then the selection process is simple and fast, but the proportion of selected data elements cannot be guaranteed to be exact
Solution Approach 1:
The system performs preliminary actions by generating random values and computing rankings before the actual selection process. The threshold is pre-calculated based on the desired proportion, and all data elements are pre-evaluated and sorted. This preparation enables exact proportion control during selection without requiring complex real-time adjustments, thus resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent replaces traditional mechanical random selection with a deterministic ranking-based system. Instead of relying on pure randomness that cannot guarantee proportions, the system uses ranked random values compared against a predetermined threshold. This substitution maintains the randomization benefit while achieving exact proportion control, addressing the precision-complexity tradeoff.
2Reliability
If randomized selection is performed without proportion control, then the selection process is efficient, but biases and drift occur over time
Solution Approach 1:
The system incorporates feedback mechanisms where the predetermined threshold is based on the desired proportion, and the ranking of random values provides implicit feedback about selection distribution. This feedback loop ensures that over time, the selected subset maintains the desired proportionality, preventing drift and biases while preserving selection efficiency through the deterministic threshold comparison.
3Adaptability or versatility
If a randomization scheme is generated and distributed across multiple computing devices, then scalability is improved, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the randomization scheme into components that can be distributed across multiple computing devices. The ranking of random values and the predetermined threshold can be independently computed and distributed to different devices, allowing each device to perform local selection operations. This segmentation enables scalability while managing complexity through modular distribution of computation tasks.
Data Source
AI summary
Data selection systems and techniques are described. For instance, a system receives a data set with a plurality of data elements. The system generates a plurality of random values. The system generates a ranking of the plurality of random values. The system selects a subset of the plurality of data elements based on a comparison between the ranking and a predetermined threshold. The predetermined threshold is based on a predetermined proportion of the plurality of data elements to be included in the subset. The system outputs a randomization scheme that includes the ranking and the predetermined proportion, wherein a second selection of a second subset of the plurality of data elements is based on the randomization scheme. The system output an indication of the subset and the second subset.