Commutative Digesting for Multi-Level Data Structure Comparison
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Solution Overview
Problem
Existing comparison tools face difficulties in efficiently comparing multi-level data structures containing unordered data collections, especially when dealing with complex and large datasets, as they often require sorting, which becomes impractical for nested collections.
Innovation Solution
The method involves determining a data structure digest value for each multi-level data structure using commutative or noncommutative accumulator functions based on whether the collection is unordered or ordered, respectively, and comparing these digest values to evaluate similarity, allowing for efficient comparison without the need for sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing comparison tools iterate over data structures to compare elements at the same position, then comparison is efficient for ordered data, but it fails to correctly compare unordered collections where element order has no meaning
Solution Approach 1:
The patent changes the parameter of comparison from position-based to content-based by using commutative accumulation functions. Instead of comparing elements at the same position, the system accumulates digest values of all elements in a collection using commutative operations, making the comparison independent of element order while maintaining accuracy for unordered collections
2Ease of operation
If data collections are sorted based on arbitrary order before comparison, then existing comparison tools can work, but it becomes impractical for multi-level embedded collections
Solution Approach 1:
The patent replaces the mechanical sorting process with a mathematical accumulation process. Instead of physically sorting elements (which becomes complex for multi-level nested collections), the system uses commutative accumulation functions to compute digest values that inherently handle unordered collections at any nesting level without requiring explicit sorting operations
3Reliability
If sorting is applied to unordered collections before comparison, then element order becomes consistent, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary action by computing commutative digest values for collections before the actual comparison. The commutative accumulation function pre-processes the unordered collections into normalized digest representations that can be directly compared without requiring sorting, thus maintaining reliability while eliminating the time-consuming sorting step
Data Source
AI summary
Techniques are provided for comparing multi-level data structures using commutative digesting for unordered data collections. One method comprises obtaining two multi-level data structures, wherein at least one multi-level data structure comprises an unordered data collection; determining a data structure digest value for each the two multi-level data structures by accumulating a data element digest value for each data element of the respective multi-level data structure, wherein a data element digest value for a given data element comprising an unordered data collection is determined using a commutative accumulator function; and evaluating a similarity of the two multi-level data structures by comparing the respective data structure digest values. A data element digest value for a given data element comprising an ordered data collection can be determined using a noncommutative accumulator function that aggregates a digest value for each data element using a noncommutative operation.