Data Analyzer Highlighting for Outlier-Focused UI Visualization
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Solution Overview
Problem
Existing data analytics software lacks effective features for presenting analytical data in a user interface, hindering users' understanding of the data, which is crucial for setting up further analytics tasks.
Innovation Solution
The Data Analyzer Highlighter tool provides statistical selection, sorting options, and highlighting features to enhance data visualization in user interfaces, allowing users to understand data better by highlighting key information such as outliers, extreme values, and correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data analytics software presents raw data in traditional formats, then the system maintains simplicity in the user interface, but users cannot efficiently understand or identify key patterns in the data
Solution Approach 1:
The patent applies color coding to highlight different statistical properties of data points. Background colors indicate outlier status, while text colors indicate extreme values. This visual encoding allows users to quickly comprehend data characteristics without adding complex interface elements, directly resolving the contradiction between information preservation and interface simplicity.
Solution Approach 2:
The system automatically calculates statistical metrics (mean, standard deviation, min, max) and prepares highlighting data before user interaction. This preliminary processing occurs in the background, so when users view the data, the analytical insights are already prepared and displayed without requiring users to perform complex operations, thus maintaining interface simplicity while enhancing data understanding.
2Loss of information
If the system highlights all statistical information, then users gain comprehensive data understanding, but the user interface becomes cluttered and harder to read
Solution Approach 1:
The patent applies different visual qualities to different parts of the data presentation. Only statistically significant features are highlighted with distinctive colors, while normal data points remain in standard format. This selective application of visual emphasis preserves readability for the majority of data while providing enhanced information where needed, resolving the contradiction between comprehensive information and interface clarity.
Solution Approach 2:
The system highlights only the most important statistical features (outliers and extreme values) rather than all possible statistical measures. This partial highlighting approach provides sufficient data understanding without overwhelming the interface, achieving the right balance between information density and readability.
3Loss of time
If users manually analyze data to identify patterns and outliers, then the system requires minimal processing resources, but users spend excessive time understanding the data
Solution Approach 1:
The system performs statistical calculations (mean, standard deviation, outlier detection) in advance before the user views the data. These computations occur in the background during data loading or initial processing, so when users interact with the interface, the analytical work is already complete. This eliminates the need for users to manually analyze data while the processing occurs during less critical system periods.
Solution Approach 2:
The data automatically highlights its own statistical features without requiring user intervention or external analysis tools. The system serves itself by computing and displaying statistical metrics autonomously, reducing both user time investment and the need for additional processing power during user interactions.
Data Source
AI summary
A data analyzer highlighter highlights elements of a user interface to enable a user to better understand and analyze the data presented. To do this, a first visualization is generated in a user interface. A configuration panel including elements for selecting statistical techniques is also generated in the user interface. Selections are obtained via the user interface of one or more statistical techniques. Then statistics are determined from the dataset using each of the one or more selected statistical techniques. Rows of data or the columns of data are then sorted based on a number of extreme values in the particular row or column, wherein the extreme value is a minimum value, a maximum value, or an outlier value. A second visualization sorted based on the number of extreme values in the particular row or column is then generated in the user interface.