String Transformation Sub-Program Suggestion via Search Space Reduction
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Solution Overview
Problem
In computing contexts, users face challenges when specifying declarative constraints for string transformations, as natural language synonyms and imprecise inputs are often not supported, leading to cumbersome programming-by-example approaches, especially for large and diverse datasets.
Innovation Solution
A context-aware method is introduced to suggest string transformation sub-programs that tolerate inexact user inputs and synonyms, reducing the search space based on input and output characteristics, datatypes, and cluster identification, thereby suggesting appropriate sub-programs for transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users specify exact declarative constraints for string transformations, then transformation accuracy is improved, but user input complexity and difficulty increase
Solution Approach 1:
The patent introduces an intermediary system that translates natural language user inputs into precise transformation constraints. This mediator layer allows users to express their intent in simple terms while the system handles the complexity of mapping these inputs to accurate transformation operations, resolving the contradiction between precision and ease of use.
Solution Approach 2:
The system performs self-service by automatically inferring transformation constraints from user inputs and dataset characteristics without requiring users to specify every detail manually. The system serves itself by generating appropriate transformation sub-programs based on analyzed patterns, reducing user burden while maintaining accuracy.
2Ease of operation
If programming-by-example is used for string transformations, then ease of use is improved, but computational load and processing time increase for large datasets
Solution Approach 1:
The patent extracts and analyzes only the necessary characteristics from user inputs and dataset samples to infer transformation constraints, rather than processing entire large datasets. This extraction approach maintains ease of use through programming-by-example while significantly reducing computational load by focusing only on relevant features.
Solution Approach 2:
The system performs partial action by analyzing a representative subset of the dataset rather than the complete dataset to infer transformation patterns. This partial processing approach provides sufficient information for accurate transformation while avoiding the excessive computational burden of processing all data points.
3Measurement precision
If comprehensive search space is used for sub-program selection, then suggestion accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent segments the search space for sub-program selection by categorizing transformations based on dataset characteristics and user input patterns. This segmentation allows the system to search within smaller, more targeted subsets of possible transformations rather than the complete search space, maintaining accuracy while improving processing speed.
Solution Approach 2:
The system performs preliminary action by pre-analyzing dataset characteristics and user inputs to narrow down the search space before selecting transformation sub-programs. This preliminary filtering step prepares the system to quickly identify relevant transformations without exhaustively searching the entire search space, balancing accuracy and speed.
Data Source
AI summary
Examples are disclosed herein that relate to string transformation sub-program suggestion. One example provides, at a computing device, a method comprising receiving a dataset having a plurality of input strings; receiving a declarative user input associated with a desired string transformation sub-program to be performed using the dataset as input; in a search space having a plurality of string transformation sub-programs, reducing the search space based on (i) the user input and (ii) one or more of the plurality of input strings of the dataset, to thereby form a reduced search space, the reduced search space having at least one string transformation sub-program that is configured to transform one or more of the plurality of input strings of the dataset; and selecting, from the reduced search space, one or more suggested string transformation sub-programs.