Hexacloud Hyperoctree for Spatiotemporal Data Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional distributed systems for managing spatiotemporal data in collaborative networks require significant memory resources to handle static spatial dimensions and dynamic temporal changes, leading to inefficiencies and redundant data storage, especially when agents with different data resolutions operate together.
Innovation Solution
The implementation of a hyper-octree data structure, referred to as a hexacloud system, which uses a unified space-time container to efficiently manage spatiotemporal data by allowing non-uniform resolutions, reducing memory consumption through symmetry-based bit-wise operations and adaptive data structure expansion, and facilitating multi-resolution data management across agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional distributed systems use uniform memory allocation for spatial dimensions, then data structure simplicity is maintained, but memory resources are wasted on static empty spaces
Solution Approach 1:
The patent segments the three-dimensional spatial data structure into multiple octants (8 sectors), each representing a specific spatial quadrant. This segmentation allows the system to manage only the portions of space that actually contain data, rather than allocating memory uniformly across the entire spatial domain. The octant-based division enables efficient memory utilization by focusing resources on active regions while excluding empty spaces from memory allocation.
Solution Approach 2:
The patent introduces a temporal dimension to the traditional spatial data structure, creating a four-dimensional spatiotemporal framework. By adding time as a fourth dimension, the system can differentiate between static spatial allocation and dynamic temporal data streams. This dimensional extension allows the system to manage spatial and temporal data separately, improving memory efficiency by allocating resources based on both spatial occupancy and temporal activity patterns.
2Adaptability or versatility
If agents with different data resolutions operate together, then system versatility is improved, but data management complexity increases due to non-uniform data granularity
Solution Approach 1:
The patent implements local quality by allowing different spatial resolutions at different locations within the same data structure. Each octant can independently manage data at its own resolution level, accommodating agents with varying sensor capabilities. This local differentiation enables high-resolution data management in regions where needed while maintaining lower resolution elsewhere, thereby supporting multi-resolution operations without uniform complexity throughout the entire system.
Solution Approach 2:
The patent introduces dynamic resolution adjustment capabilities that allow the data structure to adapt its granularity based on real-time operational requirements. The system can dynamically switch between different resolution levels when agents with varying data resolutions interact, enabling flexible accommodation of diverse data granularities. This dynamic adaptation mechanism resolves the complexity issue by making the resolution characteristics changeable rather than fixed.
3Measurement precision
If temporal data layers are added to handle dynamic changes, then data accuracy for movement detection is improved, but memory consumption increases
Solution Approach 1:
The patent employs a nested structure where temporal data layers are embedded within the spatial octant framework. Instead of creating separate temporal data structures that consume independent memory, the temporal dimension is nested within each spatial octant, allowing temporal data to share the same memory allocation infrastructure as spatial data. This nesting approach enables accurate movement detection through temporal layering while avoiding duplicate memory allocation.
Solution Approach 2:
The patent implements preliminary action by pre-defining the octant structure and temporal layering framework before actual data collection begins. The memory allocation strategy is prepared in advance based on the octant division, allowing the system to efficiently manage temporal data as it arrives without requiring dynamic memory expansion. This preliminary structuring enables accurate temporal-spatial data management while controlling memory consumption through predetermined allocation patterns.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to improve spatial-temporal data management. An example apparatus includes a hypervoxel data structure generator to generate a root hexatree data structure having sixteen hypernodes, an octree manager to improve a spatiotemporal data access efficiency by generating a first degree of symmetry in the root hexatree, the octree manager to assign a first portion of the hypernodes to a positive temporal subspace and to assign a second portion of the hypernodes to a negative temporal subspace, and a quadtree manager to improve the spatiotemporal data access efficiency by generating a second degree of symmetry in the root hexatree, the quadtree manager to assign respective hypernodes of the positive temporal subspace and the negative temporal subspace to respective positive and negative spatial subspaces.