An Indexing Method of Uncertain Moving Objects Based on Group Division

Abstract

The invention relates to a method for indexing uncertain moving objects on basis of group division and belongs to the technical field of moving object management in a computer spatial database. According to the method, on the basis of a conventional uncertain indexing structure, a group division strategy is added to solve the problem of updating cost increase caused by updating of frequency positions. Some moving objects in a set of a large quantity of moving objects are found having similar moving tracks; if the tracks are similar, the moving objects are divided into one group, as for members in the same group, all that is required is to update the position of one moving object during position updating because of similarity of position information of the moving objects, and the position information of each moving object is not required to be displayed or updated in real time. A single moving object is taken as a moving object with the similar moving tracks in the representative group, the number of times for updating of the moving objects is reduced on the basis of the updating strategy, and the position updating cost of the moving objects is reduced.

Description

technical field [0001] The invention relates to an uncertain moving object indexing method based on group division, and belongs to the technical field of moving object management in computer space databases. Background technique [0002] Traditional database indexing technology is designed to store accurate data, and its index structure stores the precise location of moving objects. Since the location uncertainty of moving objects is ubiquitous, it is necessary to improve the traditional index structure to effectively manage the uncertainty of moving objects. [0003] In order to solve the problem of how to efficiently manage the precise location information of moving objects in real time, scholars have proposed a series of index structures, which can be divided into two categories from the perspective of querying location information time: one is the index for historical location information; One is the index for the current and future location information of moving object...

AI Technical Summary

Problems solved by technology

The above index model has a good query effect for the uncertain query of moving objects with few location updates, but it cannot handle the frequent location updates of moving objects very well.

Literature [6] proposed a bottom-up update idea based on R-tree. The update process starts from the leaf nodes of the tree, which saves query time and improves the performance of dynamic update. However, the disadvantage is that the maintenance of its index requires It consumes a lot of memory resources, resulting in low system stability, and is not suitable for solving the problem of moving objects with frequent position changes and large ranges

But TPU 2 The M-tree needs additional memory space to store the information of the memorandum (UM). When the number of records in the UM gradually increases, additional space cleaning operations are required to ensure high update efficiency and when the number of uncertain moving objects is large, The probability that the updated leaf node exceeds the MBR of the old record will gradually increase, so the update efficiency will gradually decrease

[0005] Literature [10] proposed a method based on B x Indexing Strategy AB for Uncertain Moving Objects in Trees x Tree, the index model uses the combination of rectangular box inference rule and Monte Carlo simulation to predict the future position information of moving objects, and proposes efficient probability range query and probability K nearest neighbor query, but AB x The disadvantage of the tree is that the frequent update of the location information of the moving object leads to serious resource consumption and increases the update cost

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