Contextual Geohashing for Big Data Spatial Indexing
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Solution Overview
Problem
Current systems face challenges in efficiently processing and managing massive volumes of geospatial data within a temporal context, particularly in discovering, characterizing, and sustaining knowledge from Big Data, which is essential for global commerce and government entities.
Innovation Solution
The method involves constructing and displaying contextual square quadrangles with geohash code IDs and precision values, allowing for the dynamic conflation of disparate data sources using multidimensional hashing techniques, including geohashing, temporal hashing, elevation hashing, and celestial hashing, to build relationships and describe situational context for Big Data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used to manage massive volumes of geospatial data, then data storage capacity is sufficient, but data processing efficiency and knowledge discovery capability deteriorate
Solution Approach 1:
The patent segments massive geospatial data into discrete contextual square quadrangles, each with unique geohash code IDs. This segmentation allows efficient indexing, retrieval, and processing of specific spatial regions without handling entire datasets, thereby improving processing efficiency while managing large data volumes through structured division into manageable units.
Solution Approach 2:
The patent introduces temporal context as an additional dimension to traditional geospatial data processing. By integrating time-based contextual information with spatial quadrangles, the system enables multidimensional data organization and querying, improving productivity through enhanced data characterization and knowledge discovery capabilities that traditional single-dimension methods cannot achieve.
2Measurement precision
If detailed geospatial data is processed with high precision, then measurement accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent employs variable-length geohash code IDs that encode precision information directly in the data structure. By changing the parameter of code length to represent different precision levels, the system maintains measurement precision when needed while avoiding unnecessary computational complexity for lower-precision requirements, thus resolving the contradiction between accuracy and system complexity.
Solution Approach 2:
The patent uses nested contextual square quadrangles where larger geographic areas are divided into smaller precision quadrangles. This nesting structure allows the system to maintain high measurement precision for specific regions while managing overall system complexity through hierarchical organization, enabling efficient processing at multiple precision levels without requiring uniformly high complexity across all data.
3Loss of information
If multidimensional hashing techniques are applied to conflate disparate data sources, then knowledge discovery capability is improved, but data processing complexity increases
Solution Approach 1:
The patent creates a universal contextual quadrangle framework that can handle multiple types of geospatial data sources simultaneously. The same geohash-based indexing and temporal context structures work across diverse data types, improving knowledge discovery by preventing information loss while avoiding the need for separate complex processing systems for each data source, thus reducing overall processing complexity.
Data Source
AI summary
Described is a method and apparatus for constructing a boundary comprising a set of contextual square quadrangles. Also described is a method and apparatus for searching a set of contextual square quadrangles.