Multi-Product Report Interface for Filtered Temporal Graphs
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Solution Overview
Problem
Traditional data analysis methods struggle with multi-dimensional data, particularly in large volumes, as they lack the ability to effectively visualize and analyze data with multiple dimensions, leading to noisy and complex datasets that hinder actionable insights.
Innovation Solution
A system and method for generating a multi-dimensional data report using a graphical user interface that visualizes data as a two-dimensional temporal graph, allowing for filtering and grouping by dimensions, and includes a computing server to manage authorizations and store data in a multi-dimensional format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data analysis methods are used, then simplicity of analysis is maintained, but the ability to analyze multi-dimensional data effectively deteriorates
Solution Approach 1:
The patent transforms two-dimensional data representations into three-dimensional visualizations by adding a depth dimension. This allows the system to display multiple data layers and dimensions simultaneously in a unified spatial arrangement, enabling users to perceive and analyze multi-dimensional relationships that are not visible in traditional two-dimensional formats.
Solution Approach 2:
The patent implements nested data structures where multiple dimensions are organized within a hierarchical framework. Data elements are arranged in nested layers that can be selectively expanded or collapsed, allowing users to access detailed multi-dimensional information while maintaining an organized, manageable structure that does not overwhelm the user interface.
2Quantity of substance
If data volume increases, then comprehensiveness of information is improved, but data noise and complexity increase
Solution Approach 1:
The patent segments large datasets into organized dimensional categories and visual groups. By dividing data into distinct dimensional layers that can be independently manipulated and visualized, the system reduces the perception of noise while maintaining comprehensive information coverage. Users can focus on specific dimensional segments without being overwhelmed by the entire dataset.
Solution Approach 2:
The patent applies different visualization qualities and detail levels to different data regions and dimensions. Important or high-variance dimensions are displayed with enhanced detail and visual emphasis, while less critical dimensions are presented with reduced detail, creating a differentiated information presentation that reduces overall visual noise while preserving essential data volume.
3Device complexity
If data is presented in traditional two-dimensional format, then simplicity of visualization is maintained, but the ability to represent multi-dimensional information deteriorates
Solution Approach 1:
The patent extends two-dimensional visualizations into three-dimensional space by adding a depth dimension that represents additional data dimensions. This spatial extension allows the system to encode multiple data dimensions simultaneously in a unified visualization, preserving information that would be lost in traditional two-dimensional representations.
Solution Approach 2:
The patent employs asymmetric visualization techniques where different dimensions are represented with different visual weights and spatial emphases. This allows the system to maintain an intuitive user interface while accurately representing multi-dimensional information, as the asymmetric design guides user attention to the most important dimensional relationships without requiring uniform treatment of all dimensions.
Data Source
AI summary
A method for generating a multi-dimensional data report. The method includes generating a first set of data through the authorizations, and receiving a second set of data. The method includes displaying a GUI that visualizes an aggregation of the first set of data and the second set of data as a two-dimensional temporal graph. The method further includes receiving a filtering selection, filtering the aggregation of data by a first dimension to generate a first subset of data, and receiving a grouping selection to group the first subset of the data by a second dimension. The two-dimensional temporal graph includes accumulative information of the first subset of data that is separated by a plurality of second-dimensional values, each second-dimensional value of the second dimension is associated with a visual element that is distinguishable from other second-dimensional values, and a totality of the plurality of second-dimensional values forms the accumulative information.