Spreadsheet Topological Data Analysis Visualization
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Solution Overview
Problem
Existing methods for analyzing large multidimensional datasets are inefficient and fail to identify important relationships, often breaking relationships and being too sensitive to large scale distances, requiring sophisticated experts and non-interactive graphs that do not allow for exploratory data analysis.
Innovation Solution
The method involves receiving data points from a spreadsheet, mapping them to a reference space using lens and metric functions, clustering to generate a graph visualization, and providing interactive visualizations that allow for the selection and identification of data points and dimensions, enabling exploratory data analysis and visualization of relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If clustering methods are used to process large datasets, then computational efficiency is improved, but important relationships in the data are broken and lost
Solution Approach 1:
The patent applies local quality by preserving local data relationships and structures within clusters rather than treating all data points uniformly. The visualization method maintains local connectivity and relationships by creating nodes that represent data points and edges that represent relationships, allowing important local relationships to be preserved while still enabling efficient processing of large datasets through hierarchical clustering.
2Speed
If linear algebraic and analytic methods are used for data analysis, then processing speed is improved, but the methods become too sensitive to large scale distances and lose detail
Solution Approach 1:
The patent transforms the data analysis from traditional linear algebraic methods to a topological dimension where relationships are preserved. By representing data as a graph with nodes and edges, the method adds a topological dimension that captures local relationships and structures that are lost in traditional distance-based linear methods, while still maintaining computational efficiency through efficient graph algorithms.
3Loss of information
If traditional graph visualization methods are used to depict data relationships, then some relationships are shown, but the graphs are not interactive and require considerable time for experts to understand
Solution Approach 1:
The patent applies dynamics by creating an interactive visualization system where users can dynamically explore data relationships. The system allows users to interact with the graph visualizations, filter data, and explore relationships in real-time, transforming static graphs into dynamic, interactive interfaces that reduce the time required for expert analysis while preserving comprehensive relationship information.
4Productivity
If previous analysis methods are used, then data can be processed, but sophisticated experts are necessary to interpret and understand the output
Solution Approach 1:
The patent introduces an intermediary layer between data processing and expert interpretation through automated visualization and analysis tools. The system automatically processes data, generates visual representations, and provides interpretable outputs that bridge the gap between complex computational processes and human understanding, reducing the need for sophisticated expert knowledge while maintaining high data processing capability.
Data Source
AI summary
A method comprises receiving data points from a spreadsheet, mapping the data points to a reference space, generating a cover of the reference space, clustering the data points mapped to the reference space to determine each node of a graph, each node including at least one data point, generating a visualization depicting the nodes, the visualization including an edge between every two nodes that share at least one data point, generating a translation data structure indicating location of the data points in the spreadsheet as well as membership of each node, detecting a selection of at least one node, determining the location of data points in the spreadsheet corresponding to data points that are members of the selected node(s) using the translation data structure, and providing a first command to a spreadsheet application to provide a first visual identification of the first set of data points in the spreadsheet.