Hybrid Histograms for Adaptive Data Distribution Analysis
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Solution Overview
Problem
Existing histogram-producing algorithms face challenges in accurately capturing data distributions, especially in large datasets, due to increased processing overhead and complexity, and are unable to adaptively select suitable algorithms based on localized data patterns, leading to erroneous selectivity estimates and suboptimal query plans.
Innovation Solution
The approach employs a 'divide and conquer' methodology that dynamically selects histogram-producing algorithms based on skewness coefficients, such as Pearson's median skewness, to adaptively vary processing across different data set portions, allowing for hybrid histograms that combine multiple algorithms to optimize accuracy and reduce processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single histogram-producing algorithm is used for all data sets, then the algorithm can be simple and fast, but it cannot accurately capture localized data patterns leading to erroneous selectivity estimates
Solution Approach 1:
The patent applies local quality by selecting different histogram-producing algorithms for different portions of the data set based on localized data patterns. Specifically, the system divides the data set into multiple portions and applies different algorithms (e.g., equi-width, equi-height, optimal) to different portions depending on their specific characteristics, thereby achieving accurate capture of localized data patterns without requiring a single complex algorithm to handle all cases.
Solution Approach 2:
The patent implements dynamics by making the histogram-producing algorithm selection adaptive rather than static. The system dynamically selects which algorithm to apply based on characteristics of each data portion, such as data distribution patterns and skewness coefficients. This dynamic approach allows the system to respond to varying data characteristics and achieve accurate selectivity estimates while maintaining reasonable complexity through automated selection criteria.
2Measurement precision
If computationally intensive algorithms are used to improve histogram accuracy for large datasets, then measurement precision improves, but processing overhead and resource usage increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the data set into multiple smaller portions and processing each portion with an appropriate algorithm. This segmentation allows computationally intensive algorithms to be applied only to specific portions where they are most beneficial, rather than to the entire data set. The system can use simpler algorithms for portions where they suffice and reserve complex algorithms for portions that require higher accuracy, thereby balancing histogram accuracy with processing efficiency.
3Measurement precision
If multiple histogram-producing algorithms are applied to all data portions, then histogram accuracy improves, but processing overhead and resource usage increase significantly
Solution Approach 1:
The patent applies local quality by selecting different histogram-producing algorithms for different portions of the data set based on localized data patterns. Specifically, the system divides the data set into multiple portions and applies different algorithms (e.g., equi-width, equi-height, optimal) to different portions depending on their specific characteristics, thereby achieving accurate capture of localized data patterns without requiring a single complex algorithm to handle all cases.
Solution Approach 2:
The patent implements partial action by applying computationally intensive algorithms only to the extent necessary for each data portion. Rather than applying all possible algorithms to all data, the system applies the appropriate level of processing complexity only where needed, based on data characteristics such as skewness and distribution patterns. This partial application of complex algorithms reduces overall resource usage while maintaining necessary accuracy.
Data Source
AI summary
Systems and methods for producing histograms are disclosed. One method for producing a histogram includes processing a database table to produce a grouped result set, and dividing the grouped result set into a plurality of collections with each collection containing one or more intervals of the grouped result set. Further, for each collection containing one or more intervals, the method includes determining one or more statistical parameters characterizing a distribution of the data within the collection. Still further, for each collection, the method includes applying one of a plurality of different histogram-producing algorithms based on the one or more statistical parameters to produce a histogram such that at least two of the different histogram-producing algorithms are used to render a respective different portion of the histogram.