Multi-Level Fractal Grids for Hierarchical Data Storage and Retrieval
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Solution Overview
Problem
Current data storage methods lack efficient and innovative solutions for storing and accessing large amounts of data in a way that allows for easy retrieval and visualization.
Innovation Solution
The use of multi-level fractal grids in electronic storage devices, where data is organized into cells within quadrants, allowing for data manipulation, visualization, and access across different levels, with features like boundary enhancement, color association, and dual-sided data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in traditional linear formats, then storage capacity is limited, but data retrieval and visualization efficiency deteriorates
Solution Approach 1:
The patent implements a multi-level fractal grid structure where Level 0 contains the entire data set, Level 1 contains quadrants of Level 0, Level 2 contains sub-quadrants of Level 1, and so on. Each level nests within the previous level, allowing hierarchical access to data. This nesting enables efficient retrieval by allowing users to access specific portions (e.g., Level 2 Quadrant 3 Row 2 Column 2) without loading the entire data set, thus improving productivity while maintaining high storage capacity.
Solution Approach 2:
The patent transitions from traditional linear or two-dimensional data organization to a multi-dimensional fractal grid structure with multiple levels and quadrants. This dimensional expansion allows data to be organized in a hierarchical space-filling curve pattern, enabling efficient retrieval of specific data portions while maintaining comprehensive storage capacity. The multi-dimensional structure resolves the contradiction by providing both extensive storage and efficient access paths.
2Loss of information
If all data is displayed at once, then complete information is provided, but user navigation and data visualization complexity increases
Solution Approach 1:
The patent divides the complete data set into hierarchical segments organized by levels and quadrants. Level 0 represents the complete data set, which is segmented into quadrants at Level 1, which are further segmented into sub-quadrants at Level 2, and so on. Users can navigate through these segmented portions progressively, viewing only the necessary data at each level rather than being overwhelmed by the complete data set at once. This segmentation maintains data completeness while dramatically improving ease of operation through progressive disclosure.
3Device complexity
If data is organized in simple linear structures, then implementation complexity is low, but data access flexibility and multi-level navigation capability deteriorates
Solution Approach 1:
The patent implements a dynamic multi-level fractal grid structure where users can flexibly navigate between different levels (Level 0, Level 1, Level 2, etc.) and quadrants based on their specific needs. The structure allows dynamic access patterns - users can view the complete data set at Level 0, drill down to specific quadrants at Level 1, or directly access specific sub-quadrants at Level 2 or beyond. This dynamic navigability provides high adaptability and versatility while the underlying fractal mathematics provides a systematic framework that manages the apparent complexity.
Data Source
AI summary
Multi-level fractal grids allow users to store and access data. Certain cells within a grid may be configured to store two different sets of data that may be represented as two different “sides”, thus enhancing the grid's usage for storing text (e.g., reminders, classroom notes, and/or instructions).