Full-Spectrum Cardinality Estimation via Hash Bucketing
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Solution Overview
Problem
Traditional methods for estimating cardinality in big data are inefficient, requiring excessive computing resources and time, and are inaccurate across the full spectrum of cardinalities, necessitating multiple operations to compensate for inaccuracies.
Innovation Solution
The implementation of a full-spectrum cardinality estimation system that uses a single function to process compound data objects, generating hash values and automatically adjusting processing based on estimated unique objects, eliminating the need for subsequent operations and reducing overall computing cycles and processing times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear-time methods are used to identify cardinality, then accuracy is maintained, but computing resources and processing time increase tremendously
Solution Approach 1:
The patent changes the parameter of data processing from linear-time comparison to logarithmic-time hashing operations. By transforming the fundamental operation from element-by-element comparison to hash value generation and bucket placement, the system achieves both high accuracy and improved processing speed simultaneously
Solution Approach 2:
The patent replaces the mechanical linear scanning and comparison system with a hash-based probabilistic counting system. This substitution eliminates the need for direct element comparisons, reducing computational complexity from O(n) to O(n/b) where b is the bucket size, thereby improving productivity while maintaining measurement precision
2Productivity
If newer approximation methods are used to reduce computing resources, then processing time decreases, but accuracy deteriorates for low-range and full-spectrum cardinalities
Solution Approach 1:
The patent segments the hash output space into multiple buckets, where each bucket tracks the minimum hash value. This segmentation allows the system to process data in parallel while maintaining accuracy across different cardinality ranges, resolving the contradiction between computing efficiency and measurement precision
Solution Approach 2:
The patent creates a universal estimation algorithm that accurately handles the full spectrum of cardinalities (low-range, mid-range, and high-range) using the same core mechanism. The algorithm is multi-functional, adapting to different data volumes without requiring separate methods, thereby maintaining accuracy while improving overall computing efficiency
3Measurement precision
If successive operations are executed to compensate for inaccuracy, then measurement precision improves, but device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary hashing and bucket population in a single pass through the data, establishing all necessary statistical foundations upfront. This preliminary action eliminates the need for successive correction operations, reducing system complexity while maintaining measurement precision through the initial comprehensive processing
Data Source
AI summary
Systems and methods are disclosed for optimizing full-spectrum cardinality approximations on big data utilizing an optimized order statistics technique. To accomplish the foregoing, a multiset of objects that each corresponds to one of a plurality of objects associated with a resource are obtained. A compound data object is populated at least in part with data that is derived based on generated decimal fraction hash values that correspond to each object in the obtained multiset. The populated compound data object is processed with a full-spectrum arithmetic mean estimation operation that can accurately determine a cardinality estimate for the obtained multiset using less resources and time when compared to traditional techniques. The determination is further made without the need to employ linear counting or bias correction operations on low or high cardinalities. An estimated number of unique objects in the obtained multiset is determined as a result of the processing, and subsequently provided for display or further manipulation.