Transaction Data Visualization with Hierarchical Drill-Down
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Solution Overview
Problem
Existing data visualization tools are inadequate in helping users create meaningful and accurate visual representations of complex data, often overwhelming relevant information and failing to guide users in selecting appropriate visual designs and dimensions, leading to missed important data features.
Innovation Solution
A system and method for visualizing correlated transaction items and their hierarchy, involving data retrieval, dimensionality reduction, and creation of a tree hierarchy linked to user-understandable factors, enabling interaction between correlated groups and the tree hierarchy for effective data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional charting tools and Business Intelligence tools are used to represent data, then the amount of data that can be visualized increases, but the complexity of the visual representations increases and relevant information becomes overwhelmed
Solution Approach 1:
The patent segments complex data representations into multiple hierarchical levels (summary level and detailed level). The system divides the visual representation into different tiers where high-level summaries are presented first, and users can drill down to see more detailed information only when needed. This segmentation reduces the complexity of initial visual representations while still accommodating large quantities of data.
Solution Approach 2:
The patent introduces an additional dimension of interaction by implementing drill-down functionality that allows users to navigate from summary views to detailed views. This transforms the static visual representation into a multi-dimensional exploration space where users can adjust the level of detail according to their needs, effectively managing complexity while preserving data quantity.
2Ease of operation
If standardized lists of options are provided to all users, then the ease of operation improves, but the adaptability to specific user needs and data characteristics deteriorates
Solution Approach 1:
The patent implements dynamic visualization generation that adapts to each user's specific data and needs. Instead of providing static standardized options, the system dynamically analyzes the user's data characteristics and automatically generates appropriate visual representations. This dynamic approach maintains ease of operation by removing the need for users to manually select options while simultaneously improving adaptability by tailoring visualizations to specific data types and user requirements.
Solution Approach 2:
The system performs self-service by automatically selecting and configuring the most appropriate visual representations based on the data being analyzed. The algorithm autonomously determines the best chart types, dimensions, and aggregation levels without requiring user intervention, thereby maintaining ease of operation while achieving high adaptability to specific data characteristics.
3Device complexity
If a single visual design is provided to represent data, then the device complexity decreases, but the adaptability to represent various aspects of data deteriorates
Solution Approach 1:
The patent creates a universal visualization framework that can represent multiple aspects of data through a single adaptable system. The drill-down mechanism allows the same visual representation to serve multiple functions: providing summary views for high-level understanding and detailed views for in-depth analysis. This multi-functionality maintains simplicity at the interface level while enabling comprehensive data representation across different granularities.
4Ease of operation
If business measures representing a single dimension are used, then the ease of operation improves, but the measurement precision and completeness of data representation deteriorates
Solution Approach 1:
The patent segments data representation across multiple dimensional levels. Instead of relying on single-dimension business measures, the system segments information into hierarchical layers where each layer represents different dimensions and levels of detail. Users can access comprehensive multi-dimensional data while maintaining ease of operation through the structured hierarchical presentation that guides exploration from simple summaries to complex details.
Data Source
AI summary
A computer implemented method of analyzing and graphically representing the correlation of a plurality of transaction items, the method comprising the steps of: retrieving data associated with groups of the transaction items, correlating a plurality of groups of transaction items in a dimensionally reduced manner, creating a tree hierarchy which classifies the groups of transaction items in a hierarchy according to a defined user understandable factor, wherein the tree hierarchy is linked to the groups of transaction items, and graphically representing the correlated groups of transaction items and tree hierarchy to enable interaction between the correlated groups of transaction items and the linked tree hierarchy.