Local Indexes for Distributed Spatial Data Querying
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Solution Overview
Problem
Existing systems face inefficiencies in indexing and querying spatial data due to limited descriptive information, leading to inaccurate search results and increased resource usage, particularly in distributed file systems where separate indexes for raster and vector data cause redundant accesses and network communications.
Innovation Solution
A computing system generates combined spatial indexes with additional fields for both raster and vector data, splitting raster images into tiles for finer granularity, and uses local indexes in a distributed file system to reduce redundant accesses and improve query specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate indexes are used for raster and vector data in distributed file systems, then data organization is simplified, but redundant accesses and network communications increase
Solution Approach 1:
The patent combines separate raster and vector indexes into a unified spatial index structure. The indexing system integrates both data types into a single index that can be searched and accessed together, eliminating the need for separate index maintenance and reducing redundant network communications when querying spatial data.
Solution Approach 2:
The unified spatial index serves multiple functions simultaneously: it indexes both raster and vector data, supports various query types (spatial joins, proximity searches, containment queries), and works across distributed file systems. This multi-functional index reduces the need for specialized access paths for different data types.
2Measurement precision
If comprehensive descriptive information is added to spatial data, then search accuracy improves, but data storage requirements and indexing complexity increase
Solution Approach 1:
The patent segments comprehensive spatial data into hierarchical levels of detail. The index structure divides spatial data into coarse-grained regions and fine-grained features, allowing queries to first filter using simplified regional information and then retrieve detailed descriptive information only for relevant results, maintaining search accuracy while managing complexity.
Solution Approach 2:
The indexing system performs preliminary organization of spatial data by pre-computing and storing key descriptive attributes (such as bounding boxes, feature types, and summary characteristics) in the index structure. This preliminary action enables accurate filtering and searching without requiring all detailed information to be processed during query execution.
3Measurement precision
If multiple redundant searches are executed to cure inaccuracies, then search result accuracy improves, but computing system efficiency and resource usage decrease
Solution Approach 1:
The unified spatial index incorporates feedback mechanisms where query results and access patterns are used to refine and optimize the index structure over time. The system learns from search inaccuracies and adjusts indexing strategies, reducing the need for multiple redundant searches while maintaining or improving result accuracy.
Solution Approach 2:
The index structure performs preliminary filtering and ranking based on available descriptive information, providing accurate enough results for many queries without requiring subsequent verification searches. This preliminary action with built-in accuracy reduces the frequency of redundant follow-up searches.
Data Source
AI summary
Systems, methods, and other embodiments associated with indexing spatial data in a distributed file system using local indexes are described. In one embodiment, a method includes dividing the spatial data into splits. Each of the splits includes a portion of the spatial data and includes additional sub-units that further sub-divide the spatial data. The example method may also include populating index records for the sub-units using metadata extracted from the spatial data. The metadata for each of the sub-units describes sub-unit characteristics of a respective one of the sub-units and split characteristics of one of the splits associated with the respective one of the sub-units. The method includes indexing the spatial data by generating local indexes using the index records with one of the local indexes for each of the splits and generating a global index that identifies the local indexes.