Spatio-temporal data indexing method in non-relational database

Abstract

The invention discloses a spatio-temporal data indexing method in a non-relational database. A spatial index based on a quadtree and a Hilbert curve is established for spatio-temporal data in a distributed environment; temporal attributes of the spatio-temporal data are combined to form a unified spatio-temporal index; the spatio-temporal index is divided into two parts of partition keys and sorting keys, and consistent hash is used to realize distributed storage and routing of spatial data in a dynamic network topology; and when a query request is received, the partition keys and the sorting keys of a query range are determined according to temporal and spatial ranges, retrieval is carried out in storage nodes, and a query result is returned. According to the method, the storage and query efficiency of the spatio-temporal data is improved, each storage node only needs to maintain allocated data thereof, addition or deletion of the storage nodes will not cause an index error, real-time performance requirements on data accessing under a high-concurrency condition are met, and the database extendibility is good.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for efficient storing and accessing large amounts of space by assigning each location an identifiable label based upon their current position within it's geographic area. It also uses partitions between different locations to efficiently store and retrieve this data without causing incorrect indexes due to adding/removing objects from other areas. Additionally, there can be multiple copies of these data across various servers if needed. Overall, this system makes searching and retrieving data faster than previous methods while still providing accurate results over long periods of time.

Problems solved by technology

Technological Problem addressed in this patents is how to efficiently manage and process huge amounts of temporal and spatially correlated data without overwhelming up any available computing devices at home, school settings, industrial environments, and even within mobile apps. Current solutions involve storing them locally, limiting their usages, requiring expensive hardware infrastructure, slowdowns during updates, and increasing overhead expenses associated with accessing and analyzing them manually. There is currently no solution for efficient handling and deployment of spatial datasets containing temporally varying data called “spat” type assets.

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