Automated Data Visualization Combination via Common Key Matching

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Solution Overview

Problem

Existing systems require technical and business knowledge to combine multiple data visualizations, making the process time-consuming and cumbersome, often resulting in less valuable combined visualizations due to the need for manual data set manipulation and formatting.

Innovation Solution

A system that allows users to automatically combine multiple data visualizations by dragging one visualization onto another, using a common identifier, and provides cues for modifying the type, format, and scope of the combined visualization, eliminating the need for manual data set combination.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If users manually combine multiple data sets to create combined visualizations, then the ability to create customized visualizations is improved, but the time required and technical knowledge needed increase significantly

Engineering Contradiction:

Improvecustomization capabilityVSAvoidtime required

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system enables data sets to automatically combine themselves through machine-learning algorithms that identify relationships and common identifiers without requiring manual user intervention. The automated combination process performs data matching, formatting, and visualization generation autonomously, eliminating the time-consuming manual operations while preserving customization capabilities through configurable parameters.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual process of data combination with an automated computational system using machine-learning algorithms. The system automatically performs data matching, relationship identification, and visualization generation, substituting human manual operations with intelligent automated processing that reduces time requirements while maintaining versatility.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If users manually format combined data sets for viewing, then the precision of data presentation is improved, but the complexity of the process increases

Engineering Contradiction:

Improvedata presentation precisionVSAvoidprocess complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system automatically performs data formatting and presentation optimization through machine-learning algorithms that analyze the combined data sets and generate appropriate visualizations. The automated formatting process handles data alignment, scaling, and presentation formatting without requiring manual user intervention, thereby reducing process complexity while maintaining precision through algorithmic optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts formatting parameters such as data scaling, visualization type, and presentation format based on the characteristics of the combined data sets. The machine-learning algorithms automatically optimize these parameters to achieve precise data presentation without requiring users to manually configure complex formatting settings, thereby reducing process complexity while maintaining high precision.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If automated combination is implemented without requiring users to know data source structure, then ease of operation is improved, but the reliability of combination may be compromised

Engineering Contradiction:

Improveuser accessibilityVSAvoidcombination accuracy

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent replaces manual user judgment with machine-learning algorithms that automatically analyze data source structures, identify relationships, and validate combinations. The system performs automated data matching, relationship verification, and combination validation, ensuring reliable results while maintaining ease of operation by eliminating the need for users to understand complex data structures.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where machine-learning algorithms continuously learn from combination results and user interactions. The system validates automated combinations against established criteria and provides feedback loops that improve combination accuracy over time, ensuring reliable results while maintaining user-friendly operation without requiring knowledge of data source structures.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11361486B2Automated combination of multiple data visualizations

Publication Date: 2022.06.14 DOMO

AI summary

A visualization combination engine may be used to combine a first data visualization based on a first data set with a second data visualization based on a second data set. The combination process may be initiated by, for example, clicking and dragging the first data visualization onto the second data visualization. The visualization combination engine may create the combined data visualization without requiring the user to manually combine the first and second data sets. The combination may be carried out by identifying a key that is common between the two data sets and combining the first and second data sets into a combined data set based on the key, and then creating the combined data visualization based on the combined data set. One or more cues may be used during the process to provide helpful information and/or allow user selection of the properties of the combined data visualization.