Data Spreading Function for Multidimensional Chart Visualization
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Solution Overview
Problem
Enterprise software systems face challenges in simplifying data representation and visualization, particularly in generating reports that effectively convey complex, multidimensional data to users with minimal user interaction, often resulting in cluttered and confusing visualizations.
Innovation Solution
The implementation of a data spreading function within a graphical user interface that allows users to select data elements and apply data spreading models, such as relative proportional, equal, and straight line models, to generate revised data charts that provide a clearer and more understandable representation of data, reducing the need for extensive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is presented in detailed multidimensional formats, then data completeness and accuracy are improved, but information overload and visualization complexity increase
Solution Approach 1:
The patent segments data by dimensions (time, category, measure) and applies different aggregation levels to different segments. The system divides the data cube into manageable dimensional slices, allowing comprehensive data representation without overwhelming visual complexity by organizing information hierarchically across multiple dimensional layers.
Solution Approach 2:
The patent adds dimensional context to data visualizations by incorporating multiple dimensions (time, category, measure) into the charting system. Instead of presenting flat 2D data, the system utilizes dimensional attributes to create enriched visualizations that convey complex multidimensional relationships without increasing visual clutter, effectively using dimensional information as an organizational framework.
2Adaptability or versatility
If comprehensive data analysis features are provided, then analytical capability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service functionality where the system automatically performs data aggregation, segmentation, and visualization based on user selections. The charting system autonomously processes multidimensional data, applies appropriate aggregation functions, and generates visualizations without requiring users to manually configure complex parameters, thereby maintaining high analytical capability while simplifying user interaction.
Solution Approach 2:
The patent creates a universal charting system that handles multiple data types, dimensions, and aggregation functions through a single interface. The system provides multi-functional capabilities to analyze various dimensional combinations (time-series, categorical, hierarchical) using the same operational paradigm, eliminating the need for users to learn different interaction methods for different analytical tasks.
3Measurement precision
If detailed data elements are displayed, then data recognition precision is improved, but information overload increases
Solution Approach 1:
The patent applies local quality by allowing different levels of data detail to be displayed in different regions or contexts of the visualization. The system can show aggregated summary data in overview areas while providing access to detailed drill-down information when needed, ensuring that each local area of the visualization contains the appropriate quantity of information for its specific purpose without overwhelming the entire display.
Data Source
AI summary
Techniques are described for spreading the data into statistically meaningful visualizations and generating reports. A method comprising providing a graphical user interface displaying a data chart and a plurality of available options for data spreading models, receiving a first user input via the graphical user interface selecting one or more data elements of the data chart, receiving a second user input via the graphical user interface selecting a data spreading model from among the plurality of available options for data spreading models, generating a revised data chart based on the one or more data elements of the data chart in accordance with the data spreading model, wherein the revised data chart renders the one or more data elements of the data chart in a modified representation within the chart in accordance with the data spreading model, and outputting for display the revised data chart.