Bi-directional Grid Visualization Architecture
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Solution Overview
Problem
Customer management systems lack integrated, interactive visualizations within grid data views, making it difficult for users to derive insights from data without switching contexts or requiring additional software modules.
Innovation Solution
The architecture allows multiple visualization types to be attached to data grids, enabling in-context rendering of charts and other visualizations alongside grid data, with bi-directional updates and seamless interaction across single-server, multi-server, and internet deployments, using MIME format for efficient drill-downs and consistent codebase across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is presented in tabular grid form, then data storage and retrieval capability is improved, but user understanding and analysis of data trends becomes difficult
Solution Approach 1:
The patent combines tabular grid data presentation with graphical visualizations (charts, graphs, gauges) within the same user interface context. The visualization component merges with the grid to provide both detailed data views and graphical representations simultaneously, allowing users to see trends and patterns without leaving the grid context.
Solution Approach 2:
The patent adds a visual dimension to the traditional two-dimensional tabular data by incorporating graphical elements that represent data trends, patterns, and relationships. This transforms the data presentation from purely numerical/tabular to include visual representations that enhance human comprehension of complex data sets.
2Ease of operation
If graphical visualizations are added to enhance data understanding, then data analysis capability is improved, but system complexity and performance overhead increase
Solution Approach 1:
The patent implements a universal visualization framework that can render multiple types of visualizations (charts, graphs, gauges, maps) using a common infrastructure. The visualization component is designed to be multi-functional, supporting various data types and visualization styles through a unified architecture that reduces overall system complexity.
Solution Approach 2:
The visualization system automatically generates appropriate visualizations based on the grid data without requiring separate configuration or additional software modules. The system self-adapts to the data structure and selects appropriate visualization types, reducing the complexity burden on the user and integrating seamlessly with the existing grid infrastructure.
3Loss of information
If traditional reporting solutions are used for data analysis, then some insights can be derived, but tight integration with grid and interactivity are lacking
Solution Approach 1:
The patent merges the reporting/visualization functionality directly into the grid interface, eliminating the need for separate reporting tools. The visualization component is integrated at the UI level with the grid, allowing users to switch between detailed grid views and graphical representations without context switching or additional software.
Solution Approach 2:
The visualization system is designed to be dynamic and interactive, responding to user actions such as filtering, sorting, and drilling down into data. The visualizations update automatically as grid data changes, and users can interact with the visualizations to explore data from different perspectives, enhancing both adaptability and versatility.
Data Source
AI summary
Architecture that facilitates the attachment of multiple visualization types (e.g., charts) to the data of a grid and the presentation of the visualization next to the grid. The infrastructure allows each data view to be associated with a set of visualizations. These visualizations are rendered along with the grid (in a separate presentation pane) so that the user does not have to switch context to perform analysis. The visualizations available include charts that show aggregate and/or non-aggregate data. In a server farm scenario, all user interactions such as drill-downs are provided. Server farm drill-downs for these visualizations can be obtained using MHTML (MIME hypertext markup language). The visualizations can be automatically updated when filters are applied on the grid, and when drill-down is performed on the visualization, the grid is automatically updated. The visualizations can be consumed from a web-based browser client and/or thick client, for example.