Code Conversion Apparatus for Reducing Key Column Computation Time
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Solution Overview
Problem
Existing feature amount generation processing for machine learning is time-consuming due to redundant processing caused by multiple key columns in two-dimensional array data, leading to increased computation time.
Innovation Solution
A code conversion apparatus and method that detect and convert code blocks to reduce the number of key columns by selecting relevant columns for a link table, thereby aligning second code blocks with third code to enhance computation speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If grouping computation is executed for each combination of key columns in two-dimensional array data, then comprehensive feature amount generation is achieved, but computation time increases due to redundant processing
Solution Approach 1:
The patent merges multiple redundant grouping computations into a single unified computation by identifying that multiple key column combinations operate on the same two-dimensional array data. By combining these operations, the system eliminates redundant processing while maintaining comprehensive feature amount generation, thus reducing computation time without sacrificing completeness
Solution Approach 2:
The invention creates a universal processing mechanism that handles multiple key column combinations through a single grouping computation framework. The system identifies commonalities across different key column sets and implements a multi-functional computation approach that serves multiple feature generation purposes simultaneously, thereby reducing overall computation time while maintaining comprehensive coverage
2Adaptability or versatility
If multiple key columns are used for grouping computation, then more comprehensive feature extraction is achieved, but the number of processing combinations increases
Solution Approach 1:
The patent segments the processing complexity by separating the identification of redundant combinations from the actual grouping computation. The system first analyzes key column combinations to identify redundancies, then executes only the necessary computations. This segmentation reduces processing complexity while maintaining comprehensive feature extraction capability
Solution Approach 2:
The invention performs preliminary analysis of key column combinations before executing grouping computations. By pre-identifying redundant combinations and determining the optimal set of computations needed, the system reduces processing complexity in advance while ensuring comprehensive feature extraction is achieved through the necessary computations
Data Source
AI summary
A code conversion apparatus including: a detection unit that detects first code that includes first function code; an extraction unit that extracts, from the detected first codes, second codes; a selection unit that selects key columns to be used for a link table that is obtained by reducing the number of key columns of the target two-dimensional array data, based on the aggregate computation code included in the second codes and key columns of the target two-dimensional array data; a generation unit that generates third code using the first function code, the selected key columns, and the aggregate computation code, and adding the generated third code at a beginning of the second codes; and a conversion unit that converts the plurality of second codes into fourth code by aligning the second codes with the third code, based on the third code.