Interactive Outlier Visualization for Large Datasets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face difficulties in interpreting and utilizing large datasets due to existing processing techniques that do not scale adequately, making it hard to determine meaningful data and requiring advanced knowledge, which often results in underutilization of collected data.
Innovation Solution
A customizable, interactive visualization system that allows users to identify outlier data within large datasets by providing a graphical representation with dynamic, adjustable features, enabling exploratory analysis of non-parametric distributions without needing extensive programming or data processing expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processing techniques are used to handle large datasets, then data processing capability is maintained at current levels, but the system cannot scale adequately for extremely large quantities of data
Solution Approach 1:
The patent transforms one-dimensional data processing into two-dimensional visualization by plotting data points on a graphical interface with x and y axes. This dimensional transformation enables users to perceive patterns, outliers, and distributions in large datasets that would be impossible to process or interpret through conventional linear processing techniques alone.
Solution Approach 2:
The patent introduces a graphical user interface as an intermediary between the raw data and the user's analytical capabilities. This visual intermediary translates complex data processing results into intuitive graphical representations, enabling users to interpret large datasets without requiring advanced programming or statistical expertise.
2Productivity
If data processing techniques are applied to large datasets, then some data processing is achieved, but users cannot characterize or understand the processed data
Solution Approach 1:
The graphical user interface serves as an intermediary that bridges the gap between raw data processing and user comprehension. It translates complex statistical processing into visual patterns that users can intuitively understand, eliminating the need for users to possess extensive programming or data processing expertise while still achieving meaningful data characterization.
Solution Approach 2:
The patent employs visual differentiation through color, shape, and size variations in the graphical representation to encode multiple data characteristics simultaneously. Users can identify outliers, patterns, and data distributions through visual cues rather than numerical analysis, making data characterization accessible to users without specialized knowledge.
3Measurement precision
If users attempt to determine meaningful data and identify outliers, then data interpretation accuracy is improved, but it requires knowledge and effort that relatively few users possess
Solution Approach 1:
The graphical user interface acts as an intermediary that performs the complex task of outlier identification and data characterization automatically, then presents the results in an intuitive visual format. This eliminates the need for users to possess extensive statistical knowledge or programming expertise while maintaining high accuracy in outlier detection through automated processing.
Solution Approach 2:
The system performs self-service by automatically detecting patterns, identifying outliers, and generating meaningful visualizations without requiring user intervention in the complex processing steps. Users simply provide data through the interface, and the system autonomously performs the analytical work, returning results that are immediately interpretable without specialized knowledge.
Data Source
AI summary
Techniques are provided for processing, visualizing, interpreting, and otherwise utilizing collected data. More particularly, collected data may be visually represented in an interactive manner, which allows a user, for example, to define and identify outlier data within a very large dataset. These results may be obtained through the use of a customizable, interactive visualization of the data, in which outliers and other aspects of the data are visually apparent.