Bias Correction Table for HyperLogLog Estimator Accuracy
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Solution Overview
Problem
Existing estimators, such as the HyperLogLog (HLL) estimator, exhibit significant bias towards overestimation when determining small numbers of distinct values in a multiset, leading to inefficient use of computing resources and inaccurate results.
Innovation Solution
A computer system is configured with a bias table and a bias corrector to correct the estimation bias, using optimized entries with predetermined confidence intervals and interpolation methods to provide accurate estimates of distinct values, thereby reducing computational expense and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If HLL estimator is used to estimate distinct values, then memory usage is reduced significantly, but estimation accuracy deteriorates for small numbers of distinct values due to overestimation bias
Solution Approach 1:
The patent introduces a bias correction table as an intermediary component that mediates between the HLL estimator and the final result. The table stores pre-computed bias values that are looked up based on the HLL estimate, allowing the system to correct the overestimation bias without requiring additional complex computation or memory beyond the compact bias table
Solution Approach 2:
The patent changes the parameter state by transforming the raw HLL estimate through a bias correction function. The correction process adjusts the estimated cardinality by applying a scaling factor derived from the bias table, effectively transforming the biased parameter into a corrected parameter that accurately reflects the true distinct value count
2Measurement precision
If exact distinct value counting is performed, then measurement precision is improved, but computing resources are excessively consumed and memory grows unbounded
Solution Approach 1:
The patent segments the problem into two parts: (1) use the HLL estimator for rapid, memory-efficient initial estimation, and (2) apply bias correction from a pre-computed table for accuracy improvement. This segmentation allows the system to avoid the resource-intensive exact counting process while still achieving acceptable accuracy for small cardinalities
3Measurement precision
If multiple distinct value estimators are maintained to address bias, then estimation accuracy for small values is improved, but device complexity increases
Solution Approach 1:
The patent extracts the bias correction information from the complex process of maintaining multiple estimators and places it into a simple, pre-computed lookup table. This extraction allows the system to use a single HLL estimator while still achieving accurate results for small cardinalities by applying the pre-computed correction factors
Data Source
AI summary
Bias correcting system for small number estimators. A computer system includes a distinct value estimator configured to estimate a number of distinct values in a data set. The computer system includes a bias table for the estimator. The bias table includes entries with values corresponding to biases caused by the distinct value estimator correlated to values corresponding to numbers estimated. The entries in the table are optimized by having a set of entries with an optimized number of biases in the entries. The biases in the entries are associated with predetermined confidence intervals. The system includes a bias corrector configured to correct the number of distinct values in the multiset data estimated by the distinct value estimator set using values from the bias table to produce a corrected value. The system includes a user interface coupled to the bias corrector configured to output the corrected value to a user.