Adaptive Fuzzy Stratified Sampling for High-Dimensional Data
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Solution Overview
Problem
Existing data analysis methods for online advertising campaigns face challenges in accurately and quickly making decisions about the tradeoff between reachability and performance, particularly in high-dimensional datasets where uniform sampling performs poorly and fails to represent the larger dataset effectively.
Innovation Solution
The implementation of adaptive fuzzy fallback stratified sampling, which involves partitioning feature vectors into strata based on conditional dependencies, selecting a stratified sample proportional to stratum size, and using a distributed system architecture to ensure fault tolerance and scalability, allowing for near real-time responses and progressive result updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform sampling is used on high-dimensional datasets, then the sampling process is simple, but the sample fails to represent the larger dataset effectively
Solution Approach 1:
The patent applies segmentation by dividing the high-dimensional dataset into multiple strata based on conditional dependencies among features. Each stratum represents a subset of data with similar characteristics, allowing the sampling process to capture the underlying structure of the data. This segmentation transforms the single complex sampling problem into multiple simpler stratum-specific sampling problems, improving representativeness while maintaining manageable complexity.
Solution Approach 2:
The patent changes the sampling parameters by using adaptive sampling rates for different strata based on their instability and importance. Instead of uniform sampling, the system adjusts sampling intensity dynamically - oversampling unstable strata and undersampling stable ones. This parameter change allows the sample to better represent the dataset's structure while optimizing computational resources.
2Measurement precision
If accurate predictions are made by processing extremely large amounts of data, then prediction accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing the feature dependency graph and identifying unstable strata before the actual sampling and prediction process. This preprocessing step captures the structural relationships in the data once, allowing subsequent sampling and prediction operations to proceed efficiently. The unstable strata are identified in advance, enabling targeted sampling that maintains accuracy while reducing processing time.
Solution Approach 2:
The patent applies partial action by sampling only the unstable strata at higher rates while using lower sampling rates for stable strata. Instead of processing all data uniformly, the system focuses computational effort on the portions of data that most impact prediction accuracy. This selective approach maintains prediction accuracy while significantly reducing the amount of data that requires intensive processing.
3Speed
If decisions about reachability and performance tradeoff are made quickly, then real-time responsiveness improves, but decision accuracy may deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-computing the feature dependency graph and identifying unstable strata before the actual sampling and prediction process. This preprocessing step captures the structural relationships in the data once, allowing subsequent sampling and prediction operations to proceed efficiently. The unstable strata are identified in advance, enabling targeted sampling that maintains accuracy while reducing processing time.
Solution Approach 2:
The patent applies segmentation by dividing the high-dimensional dataset into multiple strata based on conditional dependencies among features. Each stratum represents a subset of data with similar characteristics, allowing the sampling process to capture the underlying structure of the data. This segmentation transforms the single complex sampling problem into multiple simpler stratum-specific sampling problems, improving representativeness while maintaining manageable complexity.
4Productivity
If the system is designed to handle increased query requests, then scalability improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the dataset into strata and distributing them across multiple computing nodes. Each node handles a specific portion of the stratified sample independently, allowing the system to scale horizontally by adding more nodes. This segmentation of data and computation simplifies the architecture compared to a monolithic system, as each node operates autonomously on its assigned strata.
Solution Approach 2:
The patent applies universality by designing computing nodes that can handle multiple types of query requests (counting, averaging, custom aggregations) on their assigned strata. Each node is a universal processing unit that can respond to various query types without requiring specialized handling logic, simplifying the overall system architecture while maintaining high query handling capacity.
Data Source
AI summary
Techniques and mechanisms described herein facilitate adaptive fuzzy fallback stratified sampling. According to various embodiments, an actual or estimated minimum vertex cover of a feature dependency graph representing a dataset may be determined. The dataset may include a plurality of feature vectors and a plurality of features. Each feature vector may include a plurality of feature values that correspond with the features. The feature dependency graph may represent a plurality of conditional dependencies between the features. The minimum vertex cover may designate a subset of the features for strata selection. The feature vectors may be partitioned into a plurality of strata based on the designated subset of features. Each stratum may include one or more of the feature vectors. Each feature vector may be assigned to a corresponding stratum based on the values of the designated subset of features for the feature vector.