Visual Display Component Selection for Data Variability
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Solution Overview
Problem
Users face difficulties in selecting appropriate visual objects or elements for manipulating various types of data, as existing tools lack efficient methods for automatically proposing patterns of visual interaction that suit the dimensions and variability of data sets.
Innovation Solution
A system analyzes available visual display components and data sets to suggest suitable visual display components that can receive user input for interacting with variable dimensions, allowing users to select and incorporate components that match the data's characteristics and structure, enabling manipulation and visualization of data through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select visual display components for data manipulation, then they can choose components that fit their needs, but the process becomes time-consuming and complex
Solution Approach 1:
The system automatically analyzes the data set and selects appropriate visual display components without requiring manual user configuration. The system serves itself by autonomously matching data characteristics with suitable visualization components, eliminating the time-consuming manual selection process while maintaining operational effectiveness.
Solution Approach 2:
The system performs preliminary analysis of the data set to identify its characteristics and requirements before presenting visual display options to the user. This advance preparation allows the system to pre-filter and recommend only the most suitable components, reducing the user's decision-making burden and time investment.
2Adaptability or versatility
If the system provides many visual display component options, then users have more choices for different data types, but the interface becomes more complex and harder to navigate
Solution Approach 1:
The system applies different levels of interface complexity to different user contexts. For users who need advanced customization, the full range of components is available. For users who prefer simplicity, the system automatically selects and presents only the most appropriate components, creating a simplified local view that reduces overall perceived complexity while maintaining full versatility when needed.
Solution Approach 2:
The system performs preliminary filtering of available visual display components based on the specific data set characteristics before presenting options to the user. This pre-processing step reduces the number of components the user must evaluate, presenting only those that are relevant and suitable, thereby reducing interface complexity while maintaining adaptability to different data types.
3Measurement precision
If users need to analyze complex data sets with multiple dimensions, then comprehensive analysis is achieved, but the difficulty of detecting and measuring appropriate visualization methods increases
Solution Approach 1:
The system introduces an automated analysis intermediary that acts as a bridge between the complex multi-dimensional data set and the user. This intermediary automatically analyzes the data characteristics, identifies relationships and patterns, and translates them into appropriate visual display component recommendations, making the complex analysis process transparent and manageable for users without requiring them to understand the underlying complexity.
Solution Approach 2:
The system autonomously performs the complex task of analyzing multi-dimensional data sets and identifying suitable visualization methods without requiring user expertise in data analysis. The system self-evaluates the data characteristics, determines appropriate visual representations, and presents recommendations, thereby achieving comprehensive analysis precision while eliminating the difficulty of selecting visualization methods for complex data.
Data Source
AI summary
A pattern of visual interactivity may be automatically proposed for processing a data set that exhibits dimensions of variability. A visual display component may be analyzed to assess whether it is capable of receiving user input representing changes that correspond to types of changes of the identified dimensions of the data set. A suitable visual display component may be provided as a suggestion on a user interface for potentially using the visual display component to allow a user to interact with the data. In some embodiments, a visual display component includes a number of visual objects and/or visually interactive components for use within a graphical user interface. A visual display component may include one or more analytic patterns having at least one of an equation, rule, constraint, expression or combination thereof that represents the analytic pattern. Alternatively, a visual display component may involve a visual display representation of data through visual characteristics of a graphical object.