Ranking Graphical Visualizations by Data Attributes
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Solution Overview
Problem
Current spreadsheet applications in web-based environments lack an efficient method to automatically generate visually appealing and contextually relevant graphical visualizations from data sets, often requiring manual selection and configuration by users.
Innovation Solution
A computer-implemented method that identifies data types in a spreadsheet, differentiates and partitions columns, selects suitable graphical visualizations based on data structures, and ranks them for aesthetic attractiveness and compatibility, generating the highest-ranked visualization for display in a web-based document.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection and configuration of graphical visualizations is required, then users can customize visualizations according to their preferences, but user effort and time consumption increase significantly
Solution Approach 1:
The system automatically identifies data types in spreadsheet columns, partitions data sets based on data structures, selects suitable graphical visualizations, and ranks them by aesthetic attractiveness without requiring manual user intervention. The visualization generation process serves itself by autonomously completing tasks that would otherwise require user effort.
Solution Approach 2:
The system performs preliminary analysis of data types and data structures before visualization selection. By pre-processing the data to identify types and structures, the system prepares the foundation for automatic visualization selection and ranking, eliminating the need for manual configuration during the actual visualization creation process.
2Extent of automation
If automatic visualization generation is implemented, then user effort is reduced, but the system complexity increases due to data type identification and visualization ranking algorithms
Solution Approach 1:
The system segments the data set into multiple partitions based on identified data types and data structures. By dividing the complex data analysis task into smaller partitions, the system can apply specific visualization selection criteria to each partition independently, then combine results through ranking, thereby managing complexity through systematic division of the problem space.
Solution Approach 2:
The system changes parameters by identifying and differentiating data types (numeric, text, date, etc.) and data structures within columns. By transforming raw data into categorized data with identified parameters and structures, the system creates a structured foundation that enables automatic visualization selection and aesthetic ranking without requiring complex ad-hoc analysis for each visualization task.
3Measurement precision
If data is partitioned and analyzed to determine data structures, then visualization accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary identification of data types and data structures by analyzing column information before visualization selection. This pre-processing step categorizes data into types (numeric, text, date, etc.) and identifies structures in advance, enabling faster and more accurate visualization selection without requiring complex real-time analysis during the actual visualization generation process.
Data Source
AI summary
A computer-implemented system, method and computer readable medium to generate graphical visualizations corresponding to a data set populated in a web-based document, such as a spreadsheet. The spreadsheet is accessible in an interne or cloud-based system, and enables users to automatically create graphical visualizations or representations based on recommendations by a spreadsheet application. The graphical visualizations may be automatically ranked such that the system provides the recommendations to the user for display. Automatic ranking is accomplished, for example, by determining data types from identifying column type, differentiating column types, and extracting data sets having attributes corresponding to various graphical visualizations.