Cartesian Column Visualization via Data Cloning and Joining
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Solution Overview
Problem
Financial reporting and data analysis applications face challenges in visualizing and processing cross-correlations between data columns, which become increasingly difficult as the number of related columns grows, necessitating improved data management and visualization technologies.
Innovation Solution
The implementation of a method that uses a replication module to clone data sources based on transpose factors and a join module to generate a flat, one-dimensional visualization of data from multiple sources, allowing for effective processing and display of cartesian columns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is input as distinct columns in cartesian format, then data relationships and correlations can be maintained, but visualization and processing difficulty increase exponentially as the number of related columns increases
Solution Approach 1:
The patent segments the cartesian column data into multiple cloned data sources, where each clone represents a specific combination of transpose factors. This segmentation allows the complex multi-column data to be divided into manageable units that can be processed individually through the join module, reducing the exponential processing complexity while maintaining the original data relationships.
Solution Approach 2:
The replication module creates multiple clones of the first data source based on the transpose factors. Each clone is a copy that corresponds to a specific combination of transpose factors, allowing the system to replicate and manage complex data relationships through duplication rather than direct manipulation of the original complex structure.
2Adaptability or versatility
If the number of related columns increases, then more comprehensive data relationships can be captured, but the difficulty in processing cross-correlations increases exponentially
Solution Approach 1:
By segmenting the data into cloned sources based on transpose factors, the system can handle comprehensive data relationships without processing the entire complex structure at once. Each segment (clone) can be processed independently, maintaining processing efficiency while covering comprehensive data relationships.
Solution Approach 2:
The patent transforms the data from a multi-dimensional cartesian column structure into a flattened one-dimensional format through the joining process. This dimensional transformation simplifies the data structure for processing while preserving the relationships through the cloned structure, thereby improving processing efficiency without losing data relationship coverage.
3Ease of operation
If data is visualized in a flat one-dimensional format, then processing and display are simplified, but the complexity of representing multi-dimensional data relationships is reduced
Solution Approach 1:
The replication module creates multiple clones of the data source, each representing a specific combination of transpose factors. These clones are then joined together to form the flat one-dimensional visualization. The copying process ensures that the simplified flat format retains the original multi-dimensional relationships because each clone preserves the specific factor combinations.
Solution Approach 2:
By segmenting the data into clones based on transpose factors and then joining them, the system achieves a flat one-dimensional representation that maintains data relationship accuracy. The segmentation allows the complex relationships to be preserved in the joined structure while presenting a simplified flat format for visualization.
Data Source
AI summary
Systems, methods, and computer media for visualizing data are provided herein. The described examples allow one-dimensional data visualizations generated using visualization tools and backend components to be displayed in response to a request to view data that includes one or more sets of Cartesian columns. A data visualization request can specify data for inclusion in a data visualization and properties for the visualization, including transpose factors for data sources. The data sources having transpose factors can be cloned to represent respective combinations of the data source with each transpose factor, and then joined to other cloned or non-cloned data sources to generate the resulting one-dimensional visualization.