Compressed Tree Indexing for Random Access to Multi-Dimensional Data
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Solution Overview
Problem
Existing methods for storing and retrieving multi-dimensional data, such as using hierarchical tree structures like quadtrees and octrees, are inefficient due to high memory overhead from parent-to-child pointers and lack of random access, especially when data is sparsely defined or compressed.
Innovation Solution
A method that encodes tree data structure nodes into an order representation, compresses data into variable-length portions, maps positions to a location representation, and stores both, allowing for efficient storage and retrieval of multi-dimensional data without decompressing the entire structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If hierarchical tree structures (quadtree/octree) are used to store multi-dimensional data, then the data can be organized in a structured manner, but the memory overhead from parent-to-child pointers becomes significant
Solution Approach 1:
The patent extracts the pointer information from the traditional tree structure and replaces it with an array-based indexing system. Each node is stored in an array where its position implicitly defines its parent-child relationships, eliminating the need for explicit pointer storage while maintaining the hierarchical structure.
Solution Approach 2:
The patent replaces the mechanical pointer-based navigation system with a mathematical indexing system. Instead of using physical pointers to link nodes, the system uses calculated array indices based on node positions, substituting a mechanical linking mechanism with a computational one that requires less memory.
2Quantity of substance
If the entire data structure is compressed to reduce storage space, then storage efficiency improves, but random access becomes impossible as the entire structure must be decompressed
Solution Approach 1:
The patent segments the compressed data structure into individually accessible units. Each node or subtree can be independently compressed and stored, allowing the system to decompress and access only the specific segment needed rather than the entire structure, thus maintaining both compression efficiency and random access capability.
Solution Approach 2:
The patent performs preliminary compression of individual nodes or subtrees before storage, creating a structure where each segment is independently compressed. This preliminary segmentation and compression allows for selective decompression of only the required portions when access is needed, avoiding the need to decompress the entire structure.
3Quantity of substance
If data is stored in a predetermined sequence to enable compression, then storage efficiency improves, but the data structure loses random accessibility
Solution Approach 1:
The patent introduces an additional indexing dimension that allows navigation in the compressed sequential structure without requiring sequential access. By creating a multi-dimensional index mapping system, the patent enables random access to elements stored in sequential compressed format, effectively adding a navigation layer that operates independently of the storage sequence.
Data Source
AI summary
A method of storing multi-dimensional data, the method comprises encoding a structure of nodes of a tree data structure into an order representation; compressing data of nodes of the tree data structure to create a data stream of compressed data portions of variable length in a sequence corresponding to the order represented in the order representation. Each compressed data portion is formed from the data of each node. The method further comprises mapping the positions of the data portions in the data stream to the order representation to produce a location representation; and storing the order representation, data stream and location representation. A system of storing multi-dimensional data is also disclosed, as well as a method and system for retrieving stored multi-dimensional data.