Heterogeneous Dataset Access in Matrix Programming
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Solution Overview
Problem
Conventional computing environments employing matrix-based mathematical programming languages lack a convenient method for processing statistical datasets with heterogeneous data structures, which often include multiple data types per variable across subjects.
Innovation Solution
A computer-implemented method for generating and accessing heterogeneous datasets in a matrix-based mathematical programming language, allowing for single commands to retrieve data for specific variables, subjects, or portions thereof, and providing a portion of the dataset based on these commands, utilizing commands like brace, dot, and parentheses to manage and process the data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional matrix-based programming languages are used, then numeric arrays and homogeneous data can be processed efficiently, but heterogeneous data structures like statistical datasets cannot be conveniently processed
Solution Approach 1:
The patent segments the dataset into a two-dimensional structure with rows representing subjects and columns representing variables, allowing each element to be accessed independently through row and column indices. This segmentation enables the system to handle heterogeneous data types across different variables while maintaining efficient processing through the structured array format.
Solution Approach 2:
The patent creates a universal data structure that can accommodate multiple data types (numeric, character, logical) within a single array framework. The matrix-based language is extended to universally handle heterogeneous statistical datasets through standardized indexing and access operations, making the system multi-functional for various data processing tasks.
2Adaptability or versatility
If cell arrays are used to store heterogeneous data, then any data type can be included, but the structure lacks the organization needed for efficient statistical dataset processing
Solution Approach 1:
The patent organizes heterogeneous data into a segmented two-dimensional array where rows correspond to subjects and columns correspond to variables. This segmentation provides clear structural organization while maintaining the ability to store different data types in different cells, resolving the contradiction between versatility and organizational complexity.
Solution Approach 2:
The patent transitions from a one-dimensional cell array to a two-dimensional matrix structure, adding the dimension of variable organization. This dimensional change enables systematic arrangement of heterogeneous data by subjects and variables, improving structure and accessibility while preserving data type diversity.
3Measurement precision
If multiple commands are used to access different portions of the dataset, then precise data retrieval is possible, but the number of commands increases complexity
Solution Approach 1:
The patent merges multiple data access operations into a unified indexing system where row indices and column indices can be combined in various ways to retrieve different portions of the dataset. This combining approach maintains precise data retrieval capability while reducing the number of separate commands needed, as a single indexed access operation can retrieve data for specific subjects, variables, or both simultaneously.
Data Source
AI summary
A computer-implemented method for a computing environment employing a matrix-based mathematical programming language includes: generating a heterogeneous dataset in the computing environment, where the heterogeneous dataset embodies a plurality of variables for a plurality of subjects, where each element of a respective variable has a same data type, where the variables comprise at least two data types; accessing contents of the heterogeneous dataset with a single command in the computing environment, where the single command is a brace command, a dot command, or a parentheses command; and providing in the computing environment a portion of the heterogeneous dataset based on the single command.