High-Dimensional Data Detection Algorithm for Spatial Temporal Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing visualization software struggles to efficiently detect and analyze high-dimensional data attributes, such as spatial, temporal, and gravimetric variations in multiple data objects, which is time-consuming and often subjective, making it difficult for users to identify temporal and gravimetrical changes across multiple instances of data.
Innovation Solution
A method and apparatus for high-dimensional detection of spatial, temporal, and gravimetrical attributes in plots and digital objects, allowing users to select events in a 2D or 3D visualization environment based on user-defined criteria, with an underlying algorithm identifying specific data properties across higher-order dimensions, enabling automatic detection and reporting of attribute changes over time, space, or gravity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users perform exclusive visual inspection of multiple data instances manually, then they can observe data properties, but it accumulates non-productive time and remains subjective
Solution Approach 1:
The system performs self-service by automatically detecting and identifying data objects and events without requiring manual user inspection. The algorithm autonomously processes high-dimensional data, identifies spatial, temporal, and gravimetric attributes, and generates results without human intervention, thereby eliminating non-productive time while maintaining detection accuracy.
Solution Approach 2:
The patent replaces the mechanical human visual inspection process with an automated computational algorithm. The system uses digital processing to detect and analyze data attributes across multiple dimensions, substituting manual observation with automated detection mechanisms that operate continuously without fatigue or subjectivity.
2Reliability
If users manually analyze high-dimensional data attributes, then they can identify temporal and gravimetric changes, but the process is esoteric and requires detailed user knowledge
Solution Approach 1:
The system introduces an intermediary algorithm that acts as a mediator between the complex high-dimensional data and the user. This intermediary automatically processes the data, translating complex spatial, temporal, and gravimetric relationships into identifiable events and objects, thereby reducing the need for detailed user knowledge while maintaining reliable identification.
Solution Approach 2:
The patent transforms the data representation by changing parameters into a format that is easier to process and interpret. The algorithm converts complex high-dimensional data into standardized event and object identifiers with associated attributes, making the analysis accessible to users without requiring extensive specialized knowledge while maintaining accurate event identification.
3Quantity of substance
If the system displays tremendous amounts of multi-dimensional data, then it provides comprehensive information, but it becomes difficult and time-consuming to identify specific attributes
Solution Approach 1:
The system extracts and isolates specific data objects and events from the comprehensive high-dimensional data set. By automatically detecting and separating relevant entities based on their spatial, temporal, and gravimetric attributes, the algorithm presents only the necessary information, reducing the difficulty of identification while maintaining comprehensive data coverage.
Solution Approach 2:
The patent segments the complex high-dimensional data into discrete, manageable units representing individual objects and events. This segmentation organizes the tremendous amount of data into identifiable components with clear attributes, making it less difficult to detect and measure specific properties while preserving the completeness of the original data set.
Data Source
AI summary
Methods and systems are presented in this disclosure for high-dimensional detection and visualization. Detection in a higher-dimensional domain of at least one of an event or one or more objects within a visualization environment can be performed by identifying at least one evolution of the event or a dynamic property of the one or more objects. The at least one evolution of the event or the one or more objects having the dynamic property can be displayed within the visualization environment. Appropriate operations can be initiated based on the at least one evolution of the event or the one or more objects.