Policy Based Sampling With Replacement For Large Datasets
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Solution Overview
Problem
As datasets grow in size, generating sufficiently large and random samples with replacement (SwR) becomes increasingly difficult for machine learning and data analytics purposes, particularly in distributed database systems with non-continuous identifiers, where existing methods are inefficient and resource-intensive.
Innovation Solution
A method is developed to efficiently generate SwR samples by calculating a density-based distribution of identifiers across database segments, allowing for parallel processing and minimizing resource usage, which involves determining the minimum and maximum identifiers, calculating the number of samples per segment, and joining the generated identifiers with the dataset to create a representative sample set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sampling methods are used on large datasets, then sample randomness is maintained, but resource consumption and processing time increase significantly
Solution Approach 1:
The patent divides the large dataset into multiple segments or partitions, each with its own identifier range. Sampling is performed independently on each segment using local minimum and maximum identifiers, allowing parallel processing while maintaining overall sample randomness. This segmentation enables efficient handling of trillions of records by distributing the sampling workload across multiple processing units.
Solution Approach 2:
The patent pre-calculates and stores metadata including minimum and maximum identifiers for each dataset segment before sampling occurs. This preliminary action allows the sampling algorithm to quickly determine identifier ranges and calculate sample distributions without scanning the entire dataset during the actual sampling process, significantly reducing processing time while maintaining randomness.
2Measurement precision
If uniform sampling distribution is applied across all database segments, then sampling accuracy is improved, but workload imbalance increases
Solution Approach 1:
The patent applies different sampling strategies to different segments based on their local characteristics. Each segment uses its own minimum and maximum identifiers to calculate local density and determine sample distribution. This local quality approach ensures accurate sampling representation while allowing each segment to be processed independently, preventing workload imbalance caused by uniform distribution requirements.
Solution Approach 2:
The patent dynamically adjusts sampling parameters such as sample count and density based on segment-specific characteristics like identifier range and data distribution. By changing parameters locally for each segment rather than applying a uniform sampling rate, the system achieves both sampling accuracy and balanced workload distribution across processing units.
3Reliability
If sampling with replacement is implemented on trillions of records, then statistical validity is maintained, but system resources are overwhelmed
Solution Approach 1:
The patent segments the trillion-record dataset into smaller partitions, each processed independently for sampling with replacement. This segmentation reduces memory requirements and computational overhead by limiting the scope of random number generation and identifier management to manageable segments rather than the entire dataset, maintaining statistical validity while conserving system resources.
Solution Approach 2:
The patent uses identifier ranges and density calculations to generate synthetic sample identifiers without physically copying or loading the entire trillion-record dataset into memory. By calculating representative identifiers based on segment metadata (min/max identifiers and density), the system achieves sampling with replacement statistics without the resource overhead of handling all original records.
Data Source
AI summary
A method, article of manufacture, and apparatus for process data. In some embodiments, this includes receiving a desired sample size from a user, increasing the desired sample size based on a policy, using the increased desired sample size to determine a number of samples to take from a database segment, wherein the database segment includes a portion of a dataset, generating a sub-dataset based on the determined number of samples to take from the database segment, wherein the sub-dataset only includes identifiers, joining the generated sub-dataset with the dataset, and storing the joined dataset in a storage device.