Spatial Data Compression via Meshing and Relative References
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Solution Overview
Problem
Current spatial analysis software faces challenges in efficiently processing vast amounts of spatially referenced data due to high overhead and limited scalability, requiring costly hardware and time-consuming manual pre-processing to handle the data effectively.
Innovation Solution
A computer-implemented method that compresses spatial data records by meshing space into elements, parsing spatial data records to relative spatial references and attribute values using dictionaries, reducing memory usage and enabling efficient processing without substantial pre-processing, allowing for faster operations and improved scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If spatial data records are stored with full spatial references and attribute values, then data completeness is maintained, but memory usage and processing time increase significantly
Solution Approach 1:
The spatial reference system is segmented into a global reference frame and local relative references. The global frame provides the base coordinate system while local references use relative offsets to denote positions within smaller regions. This segmentation allows the system to use fewer bytes for spatial references while maintaining the ability to accurately represent all locations through the combination of global and local reference information.
Solution Approach 2:
The patent changes the parameter representation from absolute spatial coordinates to relative spatial references combined with a global reference frame. Attribute values are transformed from their original forms to compressed representations using attribute dictionaries that map values to smaller integer codes. This parameter transformation reduces the quantity of data while preserving information completeness through the reference relationships.
2Manufacturing precision
If manual pre-processing is performed to structure spatial data, then data quality is improved, but processing time and resource consumption increase
Solution Approach 1:
The spatial data structure performs self-service by automatically organizing incoming spatial data records into the appropriate hierarchical reference framework. The system automatically determines which local references apply to each spatial record based on its coordinates, and automatically compresses attribute values using the attribute dictionaries. This eliminates the need for manual pre-processing while maintaining data quality through the structured organization and compression mechanisms.
Solution Approach 2:
The patent performs preliminary action by pre-establishing the global reference frame and attribute dictionaries before processing actual spatial data. The reference framework and compression mappings are prepared in advance, allowing incoming data to be immediately organized and compressed without manual intervention. This preliminary setup enables efficient automated processing while ensuring data quality through the pre-defined structural rules.
3Speed
If hardware optimized for spatial data processing is used, then processing speed is improved, but device complexity and cost increase
Solution Approach 1:
The patent substitutes the mechanical/hardware optimization approach with a software-based data representation optimization. Instead of using specialized hardware to process spatial data efficiently, the invention transforms the data itself into a compressed format that can be processed efficiently by standard hardware. The spatial references are represented using relative coordinates and the attribute dictionaries compress attribute values, allowing conventional processors to handle the data at high speeds without requiring expensive specialized hardware.
4Reliability
If the entire database is queried for spatial analysis, then comprehensive results are obtained, but processing time increases substantially
Solution Approach 1:
The spatial database is segmented into regions defined by the local reference frames. Each region can be independently queried and processed, allowing the system to retrieve only the relevant portions of the database needed for a specific analysis rather than scanning the entire database. This segmentation maintains analysis completeness by ensuring all relevant spatial records are found while dramatically reducing query processing time through localized search.
Data Source
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AI summary
Example embodiments relate to a computer implemented method for compressing spatial data records (220), wherein a respective spatial data record (221) comprises a spatial reference (222) within a space (218), and at least one attribute value (223) of at least one attribute type. The method comprises meshing (201) the space in meshing elements (230 - 245), wherein a relative position of a meshing element is characterized by a relative spatial reference (255). The method further comprises parsing (202) the spatial data records, comprising, for a respective spatial data record: compressing (203) the spatial reference to the relative spatial reference of the meshing element in which the spatial reference is located; and compressing (204) the at least one attribute value to at least one respective numerical value (224) according to at least one attribute dictionary (225) comprising a mapping between attribute values and numerical values for an attribute type.