Data Visualization Application Automatic Raw Data Splitting
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Solution Overview
Problem
Existing data visualization systems require users to build complex calculations and external programs to prepare raw data, which often results in syntax errors and inadequate data formatting, making it difficult to create meaningful visualizations.
Innovation Solution
A data visualization application automatically identifies raw data within columns, splits it using delimiting locations, and formats unformatted dates, simplifying the process by reducing the need for user input and manual calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If users build custom calculations and external programs to prepare raw data, then data formatting capability is improved, but system complexity and user training requirements increase
Solution Approach 1:
The system automatically performs data preparation tasks by identifying raw data columns, detecting delimiting locations, and splitting data values without requiring users to build custom calculations or external programs. The application self-services the data formatting process that previously required user intervention and expertise.
Solution Approach 2:
The system performs preliminary data preparation actions automatically before data visualization is created. By pre-identifying delimiting locations and pre-splitting data values into separate columns, the system eliminates the need for users to perform these preparation steps manually when creating visualizations.
2Ease of manufacture
If users build custom calculations to prepare raw data, then data formatting capability is improved, but syntax errors and inadequate formatting increase
Solution Approach 1:
The system automatically performs data preparation tasks by identifying raw data columns, detecting delimiting locations, and splitting data values without requiring users to build custom calculations or external programs. The application self-services the data formatting process that previously required user intervention and expertise.
3Ease of operation
If automatic data identification and splitting is implemented, then ease of data preparation is improved, but processing time increases
Solution Approach 1:
The system samples a subset of data values from each column to identify delimiting locations rather than analyzing every single value. This partial action approach maintains high accuracy in detecting delimiting patterns while significantly reducing the processing time required for automatic data preparation.
Data Source
AI summary
A computing device selects a sample of data values from an existing data column in a data source. The device generates statistics describing occurrence of consistent delimiters within the sample of data values. The device identifies a first delimiting location within the sample of data values in accordance with the generated statistics. It also creates first and second new data values by splitting each data value in the existing data column at the first identified delimiting location. It stores the first and second new data values in first and second new data columns, respectively, and assigns field names to the first and second new data columns. The device further displays, in a data visualization user interface, the assigned field names and a data visualization generated according to the first new data values based on user selection of the assigned field name of the first new data column.