Multi-Array Lookup Index Mapping for Variable Retrieval
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Solution Overview
Problem
In computer science applications, especially in application development, efficiently looking up related variables becomes inefficient with a brute force method when dealing with a large number of variables due to finite storage and processing resources, requiring a solution that considers time and space complexity.
Innovation Solution
The method involves using a collection of arrays, each with a different number of dimensions, where the number of dimensions corresponds to the number of datasets to be traversed, allowing for efficient forward and backward lookups by determining index values and returning corresponding values, thereby optimizing operations based on the number of dimensions of the first array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a brute force method is used to lookup related variables, then the lookup operation is simple to implement, but the time complexity and processing efficiency deteriorate when dealing with a large number of variables
Solution Approach 1:
The patent pre-computes and stores index mappings between related variables before the actual lookup operation. When a variable is selected, the pre-stored index values enable immediate retrieval of related variables without iterating through all possible combinations, thus resolving the contradiction between simple implementation and high efficiency.
2Reliability
If more variables are stored for comprehensive lookup, then the completeness of related variable retrieval is improved, but the space complexity and storage requirements increase
Solution Approach 1:
The patent extracts only the essential index mapping information needed for lookup operations, storing only the critical relationships between variable indices rather than storing all possible variable combinations. This selective extraction maintains retrieval completeness while minimizing storage space requirements.
3Device complexity
If a brute force lookup method is used, then no additional data structures are needed, but the processing time and latency increase significantly
Solution Approach 1:
The patent performs preliminary computation to build index mapping tables that store the relationships between related variables. During actual lookup operations, these pre-built structures enable constant-time or logarithmic-time retrieval, dramatically reducing latency while maintaining acceptable data structure complexity.
4Measurement precision
If comprehensive variable relationships are maintained for accurate lookup, then the precision of related variable retrieval is improved, but the computational resources and processing overhead increase
Solution Approach 1:
The patent extracts and stores only the essential index relationship information needed for accurate lookup, rather than maintaining complete variable relationship matrices. This selective extraction preserves lookup accuracy by storing critical mapping information while reducing computational overhead and resource consumption during both preprocessing and query operations.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: providing a plurality of arrays, wherein each array of the plurality of arrays is related to a first array; receiving, by a lookup operation, a variable value, wherein the variable value is stored in the first array; determining a first index value of an array element of the first array, wherein the array element of the first array stores the variable value; determining, based on the first index value of the array element of the first array, a respective index value for each array in the plurality of arrays; returning, to a caller of the lookup operation, a respective value stored at a corresponding array element of each respective index value for each array in the plurality of arrays.