Multi-dimensional Data Representation for Complex Object Similarity
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Solution Overview
Problem
Human-centered data exploration is challenging due to complex inter-relationships between parameters, leading to cognitive load and hindered insight generation, as existing methods fail to effectively present and visualize multidimensional data in a structured manner.
Innovation Solution
A computer-implemented system and method for multi-dimensional data representation that includes a data repository, processor, and visualization interface, capable of receiving queries, evaluating similarity measures, and generating multi-visual representations of similarity values and metadata, allowing for filtering and coordination across devices to provide a coordinated multi-visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If parameters are presented in a structured multidimensional representation, then insight generation is improved, but device complexity increases
Solution Approach 1:
The patent segments complex multidimensional data into hierarchical levels (e.g., individual parameters, parameter groups, and overall system views). This segmentation allows users to explore data at appropriate levels of detail without being overwhelmed by the full complexity, thus improving insight generation while managing system complexity through organized data structures.
Solution Approach 2:
The patent introduces additional visualization dimensions such as temporal evolution, spatial distribution, and hierarchical grouping to represent multidimensional data. By adding these representational dimensions rather than increasing computational complexity, the system enables better insight generation through enhanced data visualization and exploration capabilities.
2Loss of information
If complex inter-relationships between parameters are analyzed, then insight generation is improved, but cognitive load increases
Solution Approach 1:
The patent applies local quality by providing different levels of detail and abstraction for different parts of the data structure. Users can focus on specific parameter relationships of interest while maintaining access to broader contextual information, reducing cognitive load by allowing selective deep-dive into complex inter-relationships without requiring simultaneous processing of all data.
Solution Approach 2:
The patent introduces intermediate visualization layers and aggregation functions that mediate between raw complex data and user understanding. These intermediaries (such as summary statistics, trend lines, and grouped representations) simplify complex parameter inter-relationships while preserving essential insights, thereby reducing cognitive load during data exploration.
3Adaptability or versatility
If multidimensional data is visualized across multiple devices, then accessibility is improved, but data coordination complexity increases
Solution Approach 1:
The patent implements universal data representation formats and standardized communication protocols that enable the same multidimensional data structure to be accessed and visualized across multiple device types and platforms. This universality allows data to be portably shared between devices without requiring complex device-specific coordination, as the core data model remains consistent across the system.
Data Source
AI summary
Systems and method for multi-dimensional data representation of an object is provided. The multi-dimensional data representation method includes evaluating a similarity measure for a query corresponding to an object. The similarity measure between the objects are used to compute the similarity values corresponding to the object and based on at least one metadata dimension associated with the object. The similarity value are sorted to create a multi-dimensional array of similarity values. The similarity values are represented in a scalar form and a visualization interface displays a multi visual representation of the similarity values and data associated with the object.