Spatio-temporal data indexing method for cloud environment

Abstract

The invention provides a spatio-temporal data indexing method for a cloud environment. A double-layer indexing mode is used for supporting cross-cloud spatio-temporal range query. Wherein the double-layer indexing mode is to establish a local index for spatio-temporal data stored in cloud nodes by adopting a mode of firstly dividing time and then dividing space, and then publish an index abstractof the local index into a global index. When space-time range query is carried out, nodes participating in query are filtered out through global indexes, and then data are obtained from local indexesof the nodes.

Description

technical field [0001] The invention relates to an indexing method, in particular to a space-time data indexing method oriented to an inter-cloud environment. Background technique [0002] In the application of the Internet of Vehicles, GPS and OBD devices equipped with vehicles are used to continuously collect various data centered on location, and thus accumulate a large amount of historical spatiotemporal data. Trajectory data is one of the important categories. Considering the requirements of mass storage and high insertion rate, HBase is generally chosen to store track data, and the primary key is vehicle ID + timestamp. Using this mode to store trajectory data can easily obtain the trajectory data of vehicles in a certain period of time. However, when the application needs to query all vehicle trajectory points in a certain spatial area within a certain period of time (temporal and spatial range query) , you need to scan the entire table in a filtered manner to obtain...

AI Technical Summary

Problems solved by technology

The disadvantage of MD-HBase is that the maintenance of consistency between the index layer and the storage layer is complicated, and considering that spatio-temporal data is a special multi-dimensional data, the time dimension has no boundaries and Z-ordering technology cannot be used to reduce dimensionality, and the time dimension can only be processed separately , the time dimension has no index at this time, resulting in low query efficiency

[0005] The above can be considered as a solution in the single-cloud environment in the prior art, but when the spatio-temporal data is distributed in the inter-cloud environment, Md-HBase cannot effectively solve the cross-cloud spatio-temporal range query problem, and its shortcomings mainly include two points : 1. It is a multi-dimensional data storage and query system. Spatio-temporal data is a special multi-dimensional data, so it cannot be directly used in this system; 2. Md-HBase requires redistribution of data according to the designed primary key rules when storing data, while in There are security and data scale limitations in the inter-cloud environment, it is difficult to migrate data between clouds, and it does not allow redistribution of data within the cloud

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