Transformation Program Generation for Data Wrangling
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Solution Overview
Problem
Data transformations across diverse formats are time-consuming and labor-intensive, requiring manual searches for appropriate transformation operations, as existing tools do not efficiently adapt to user-specific data formats.
Innovation Solution
A system that dynamically generates transformation programs based on example input and output values, using a transformation engine to identify and rank relevant tools from a collection, and combines these with supplemental tools to suggest and execute data transformations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search and development of transformation operations is performed, then transformation accuracy can be achieved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs self-service by automatically generating transformation programs through machine learning models that learn from example input-output pairs. The transformation engine autonomously identifies appropriate transformation operations without requiring manual search or development, thereby maintaining transformation accuracy while significantly reducing time consumption and labor intensity.
Solution Approach 2:
The system performs preliminary action by pre-processing example input and output values to extract transformation patterns before actual data transformation. The machine learning model is trained in advance on these examples, creating a ready-to-use transformation program that can be rapidly applied to transform data from diverse formats to standardized formats without repeated manual intervention.
2Stability of the object's composition
If multiple different transformation operations are applied to diverse data formats, then data consistency can be achieved, but the complexity of the transformation process increases
Solution Approach 1:
The transformation engine implements universality by using a single machine learning model that can handle multiple different data formats and generate appropriate transformation operations automatically. Instead of requiring separate manual configuration for each format combination, the universal model learns from diverse examples and adapts to transform various formats (CSV, JSON, XML, etc.) to standardized formats, reducing process complexity while maintaining data consistency.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting transformation parameters based on learned patterns from example data. The machine learning model automatically determines appropriate transformation parameters (such as date formats, numerical precisions, string encodings) by analyzing the characteristics of input data and target output requirements, thereby achieving data consistency without manual parameter configuration for each transformation scenario.
3Productivity
If automated transformation program generation is implemented, then productivity increases, but the complexity of the system architecture increases
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a mediator between raw example data and transformation program generation. This intermediary component learns transformation patterns from example input-output pairs and translates them into executable transformation operations, thereby automating the transformation process and improving productivity while encapsulating the complexity within the intermediary model rather than requiring complex system-wide architecture.
Data Source
AI summary
Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data transformations, according to embodiments of the present invention. In one embodiment, a set of example values is received. An index to identify a plurality of data transformation tools that are relevant to the set of example values is referenced, wherein each of the data transformation tools correspond with one or more tool examples. The data transformation tools are ranked based on an extent of similarity between the set of example values and the tool examples. For data transformation tools associated with the extent of similarity that exceeds a similarity threshold, a transformation program is generated that uses the data transformation tool and a supplemental transformation tool to transform the one or more example input values to the desired form in which to transform data.