Spatio-Temporal Indexing for Skewed GPS Tracks
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Solution Overview
Problem
Existing spatio-temporal indexing schemes, such as R-trees and their variants, are inadequate in managing and retrieving GPS data due to the skewed nature of accumulated GPS tracks, leading to inefficient storage and retrieval of user experiences.
Innovation Solution
A spatio-temporal indexing scheme using compressed start-end trees (CSE-trees) is implemented, where GPS data is partitioned into segments based on a spatial grid with associated temporal indexes, allowing efficient search and compression based on update frequency to balance storage and access efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If R-tree or its variants are used for spatio-temporal indexing, then spatial range and temporal interval search functionality is provided, but the indexing scheme is not configured well for handling the skewed nature of accumulated GPS tracks
Solution Approach 1:
The patent changes the indexing parameters by introducing a temporal index that tracks upload time rather than just spatial coordinates. This temporal dimension allows the system to adapt to the skewed distribution of GPS tracks where recent uploads are more frequent than historical ones, resolving the contradiction between standard R-tree indexing and skewed data handling.
Solution Approach 2:
The system dynamically adjusts indexing strategies based on the skewed nature of GPS data by maintaining both spatial and temporal indices. The temporal index enables dynamic retrieval operations that account for the time-dependent distribution of GPS tracks, improving adaptability while maintaining indexing reliability.
2Loss of information
If GPS data is stored with full spatio-temporal detail, then complete track information is preserved, but storage space consumption increases
Solution Approach 1:
The patent extracts the temporal dimension from the traditional spatial-only R-tree indexing and creates a separate temporal index. This extraction allows the system to store spatial details completely while using the temporal index to optimize retrieval, reducing the effective storage burden for frequently accessed recent data.
Solution Approach 2:
The indexing system is segmented into spatial and temporal components. The spatial index handles complete track geometry information while the temporal index manages time-based retrieval. This segmentation allows complete information preservation without proportionally increasing overall storage requirements, as the temporal index enables more efficient data access patterns.
3Reliability
If temporal index is frequently updated, then current GPS track data is accurately reflected, but data structure complexity and storage increase
Solution Approach 1:
The patent applies local quality by differentiating the treatment of recent versus historical temporal indices. Recent temporal indices maintain full update frequency and structural complexity to ensure data currency, while historical indices can be simplified or archived. This localized approach maintains reliability for current data while reducing overall system complexity and storage.
Data Source
AI summary
Described is a technology by which uploaded GPS data is indexed according to spatio-temporal relationships to facilitate efficient insertion and retrieval. The indexes may be converted to significantly smaller-sized data structures when new updates to that structure are not likely. GPS data is processed into a track of spatially-partitioned segments such that each segment has a cell. Each cell has an associated temporal index (a compressed start-end tree), into which data for that cell's segments are inserted. The temporal index may include an end time index that relates each segment's end time to a matching start time index. Given query input comprising a spatial predicate and a temporal predicate, tracks may be searched for by determining which spatial candidate cells may contain matching results. For each candidate cell, the search accesses the cell's associated temporal index to find any track or tracks that correspond to the temporal predicate.