User Interface Data Sample Transformation for Large Datasets
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Solution Overview
Problem
Conventional data analysis tools struggle to parse extremely large amounts of unstructured data, leading to hidden patterns and errors such as program freezing and crashing, necessitating improved approaches for structuring and analyzing large datasets.
Innovation Solution
A UI sample transformer system that applies transformations to a sample of raw data in real time, allowing users to preview changes and record a transformation job for application to the entire dataset, with automatic validation and error handling for new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data analysis tools are used to parse extremely large amounts of unstructured data, then data processing capability is provided, but the system experiences program freezing and crashing, and patterns remain hidden
Solution Approach 1:
The system segments the large unstructured data into smaller manageable portions by first generating a sample dataset from the raw data. Users then perform transformations on this sample rather than the entire dataset, which prevents program freezing and crashing while still enabling analysis of the full dataset through the recorded transformation job.
Solution Approach 2:
The system performs preliminary actions by pre-processing the raw data to generate a sample dataset before applying transformations. This sample is created in advance and used for user interaction and transformation testing, while the full transformation is recorded and applied to the complete dataset automatically, ensuring stability and preventing crashes.
2Quantity of substance
If conventional data analysis tools are used to parse extremely large amounts of data, then data processing is attempted, but the data remains in unstructured form and cannot be parsed
Solution Approach 1:
The system divides the data structuring task into two stages: first, generate a sample dataset from the raw unstructured data; second, apply transformations to the sample and record the transformation job. This segmented approach enables users to work with manageable data portions while the recorded transformations are automatically applied to the full dataset, improving ease of data structuring.
Solution Approach 2:
The system creates a copy of the raw data in the form of a sample dataset, which is then used for transformation and analysis. This sample copy allows users to perform structuring operations without working directly with the entire large unstructured dataset, making the data structuring process more manageable and effective.
3Ease of manufacture
If users attempt to add structure to large amounts of data manually, then data transformation is performed, but user effort and time consumption increase significantly
Solution Approach 1:
The system segments the data transformation process by first creating a sample dataset that represents the full raw data. Users then perform time-consuming transformation operations only on this small sample rather than the entire large dataset. After transformations are applied to the sample, the transformation job is recorded and automatically applied to the full dataset, significantly reducing user time while maintaining structuring capability.
Solution Approach 2:
The system performs preliminary transformation on a sample dataset before applying transformations to the full dataset. This preliminary action allows users to spend their time interacting with and transforming only the sample data, while the recorded transformations are automatically applied to the complete dataset, minimizing user time loss.
4Productivity
If transformations are applied to the entire dataset immediately, then complete data processing is achieved, but real-time feedback and user interaction are lost
Solution Approach 1:
The system segments the data processing operation by first transforming only the sample dataset, which provides immediate real-time feedback to users. The transformation results on the sample are displayed instantly, allowing users to interact with and adjust transformations in real-time. After user approval, the recorded transformation job is applied to the full dataset in the background, maintaining both productivity and ease of operation.
Solution Approach 2:
The system implements feedback by displaying transformation results on the sample dataset immediately after transformations are applied. This real-time feedback allows users to see the effects of their transformations and make adjustments as needed. The feedback mechanism operates independently from the full dataset processing, maintaining user interaction capability while the complete data processing continues in the background.
Data Source
AI summary
An approach for transforming a large dataset using user interface-based transformations applied to a sample of the dataset is disclosed. The sample of the large dataset has the same or similar format as the large dataset. A user can quickly apply transformations to the sample dataset using UI-based instructions. The UI-based instructions can be used to create a transformation job that can be configured to run on a backed database, such as a distributed database, to apply the transformations to the large dataset.