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Road network space keyword search method

A keyword and spatial index technology, which is applied in the field of efficient spatial keyword retrieval, can solve the problems of high time and space complexity, poor scalability, and is not suitable for complex road network data, and achieves the effect of improving query efficiency.

Active Publication Date: 2015-02-25
神行太保智能科技(苏州)有限公司
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AI Technical Summary

Problems solved by technology

However, this method and other methods have a common disadvantage, that is, they are not suitable for complex or very large road network data.
They are less scalable and have higher time and space complexity

Method used

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Embodiment Construction

[0017] 为更好的理解本发明,下面首先对相应术语进行说明。

[0018] 一、路网

[0019] 此处使用一个权重图G表示路网(即road network)。G=(V,E,W),其中V表示road network中的所有顶点的集合,E表示所有的边的集合,W是G中所有边的权重的集合,即顶点对之间的距离。如υ∈V则说明υ是G中的road network中边的交集或者终点。而(υ,ν)∈E则表示(υ,ν)是路网中的某一路段,而对应的w υ,v 则表示边(υ,ν)对应的权重,即边上的距离。其中,||υ,ν||表示边(υ,ν)上的最短距离即||υ,ν||=w υ,v ,而查询q到目标o之间的最短距离为||q,o||=min(||q,υ||+||o,υ||,||q,ν||+||o,ν||)。

[0020] 二、图分割

[0021] 给定一个图G=(V,E),其中V是顶点的集合,E是边的集合,如果现在存在一个集合G={g 1 , g 2 ....g f},其中g f =(V f ,E f ),满足如下条件,则称g f 为G的一个子图:

[0022] (1).U 1≤i≤f υ i =V

[0023] (2).fori≠j,

[0024] (3) if(υ,ν)∈E,then(υ,ν)∈E i

[0025] 三、边界点

[0026] 对于原始图G的一个子图g i ,如果存在一个顶点υ∈V i ,假如存在边(υ,ν)∈E并且 则说明υ是一个边界点。通常用一个集合B(g i )来表示该子图g i 的边界点集合。因为在原始图中,有些顶点之间原来是连通的,但是经过图分割之后,被分散在不同的子图当中,这样原来某些存在原图当中的边则不存在了,但是如果需要计算两个子图之间的距离,则两个子图的边界点之间的最短距离即为两个子图的最短距离。

[0027] 四、空间文本对象

[0028] 通常用2维平面中的一个带有坐标的点,并且含有一个用来描述它的关键字集合来表示一个空间文本对象。例如o={loc,term},其中loc表示位置,loc.lat和loc.long分别表示经纬度坐标,term表示一个描述该文本的关键字的集合term={t 1 ,t 2 ....t f},简单的来...

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Abstract

The invention discloses an efficient space keyword search method on a road network. Three methods including SNE, FITG and SG-Tree are put forward, wherein the performance of the SG-Tree method is the best, and the SG-Tree method is the main method of the road network space keyword search method. Specifically speaking, according to the SNE method, corresponding signatures are built for all edges of the road network, the Dijkstra algorithm is utilized, the network is traversed in a network expanding mode, and efficiency is low; according to the FITG method, a novel space index and a text inverted index are combined, the inquiring process is executed according to the pruning principle of text first and space second in serial, the efficiency is improved greatly, but defects still exist; thus, the space index and text index signature technology is utilized, a mixed index SG-Tree is put forward, corresponding signatures are built for all joints of a space index G-Tree, whether the joints comprise targets conforming to search or not can be efficiently detected, pruning can be carried out on two dimensions of space and text at the same time, and the search efficiency is greatly improved.

Description

technical field [0001] 本发明属于空间文本索引领域,具体涉及一种利用空间索引树实现路网上的高效的空间关键字检索的方法。 Background technique [0002] 随着空间定位技术的飞速发展,移动设备(e.g,smartphones)在我们的日常生活中越来越普及,基于位置的服务也随之快速发展,其与人类的生活也越来越紧密。在日常生活中,每天通过移动设备会产生大量的带有地理位置标签的文本数据。例如,在基于位置搜索服务中(e.g,Google Maps,Yahoo!Maps etc)提供了一些目标位置信息并且带有简短的文本描述,人们可以通过这些应用程序发布带有地理位置的文本信息,其中涉及到空间关键字查询的技术。 [0003] 当前的空间关键字查询大多是在欧氏距离空间的基础上,然而,对于这些巨大的空间文本数据库,一个关键的挑战在于如何建立有效的查询处理机制能实时响应用户的查询需求。以往的查询方法大都是在欧氏距离空间下。在现实生活中,人们的日常行为受到路网的约束,目标之间的欧氏距离不同于路网中的实际距离,在路网上的两个位置之间的网络距离可能比他们之间的欧氏距离大的多。比如,位于河流两岸的两个目标之间的欧氏距离远远小于它们之间的实际网络距离,这就导致在欧氏空间下得到的结果在实际中可能并不相近,因此,我们需要新的查询处理方法,能快速找到路网上距离最近且符合文本描述的目标,如 figure 1 shown. [0004] 以往的空间关键字的研究主要是集中在欧式空间下,并不能直接应用与路网上。而传统的路网上的空间关键字查询方法是通过网络扩展的方式,时间和空间复杂度较高。近年来,有部分工作研究路网上的空间关键字查询,虽然也取得了一些成果,但是这些技术并不能高效的实现路网上空间关键字查询技术,效率低下。例如,在论文Top k Spatial Keyword Queries On Road Networks中,作者第一次提出路网上的空间关键字查询,论文中提出一种层次结构的空间索引,并对路网建立一个空间层次树,利用层的优势实现高效剪枝。但是,该方法和其他方法都存在一个共同的缺点,即都不适合复杂或者路网数据非常的大的情况。它们的扩展性较差,时间空间复杂度较高。 [0005] 鉴于上述原因,本发明利用一种新颖的空间索引结...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/29
Inventor 赵朋朋方海林许佳捷周晓方
Owner 神行太保智能科技(苏州)有限公司
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