WO2020215437A1 - 应用于电子地图的空间关键字查询的近似搜索方法 - Google Patents

应用于电子地图的空间关键字查询的近似搜索方法 Download PDF

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WO2020215437A1
WO2020215437A1 PCT/CN2019/088771 CN2019088771W WO2020215437A1 WO 2020215437 A1 WO2020215437 A1 WO 2020215437A1 CN 2019088771 W CN2019088771 W CN 2019088771W WO 2020215437 A1 WO2020215437 A1 WO 2020215437A1
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keyword
spatial
query
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keywords
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过敏意
姚斌
张建锋
林昊
陈�全
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Shanghai Jiao Tong University
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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  • the invention belongs to the technical field of positioning, and specifically relates to an approximate search method for spatial keyword query of electronic maps based on the Spark platform.
  • Spatial keyword query takes the user's geographic location information and multiple query keywords as parameters, and returns spatial objects that have spatial and text relevance to these parameters.
  • the so-called approximate keyword search refers to searching for a string similar to the query string under a given distance measurement.
  • the mainstream solution is based on a space-first index structure.
  • the space-first structure is not efficient for index optimization of spatial text objects with tens of keywords on average. Therefore, how to develop an approximate search method for spatial keyword query based on text matching priority to avoid unnecessary text matching process, reduce matching overhead, and achieve better search results. It is the direction that those skilled in the art need to study.
  • R-tree Another form of B-tree development towards multi-dimensional space, which divides space objects into ranges, and each node corresponds to a region and a disk page , The non-leaf node's disk page stores the area range of all its child nodes, and the area of all child nodes of the non-leaf node falls within its area range.
  • IR-tree Based on the inverted index and the R-tree index, the calculation model of the text similarity through the inverted index.
  • BFIR-tree IR-tree based on massive data processing
  • CBFIR-tree dynamic BFIR-tree
  • S2I-V structure model structure that should be processed differently for keywords of different frequencies
  • eBRQ based on keywords contained Range query
  • aBRQ k nearest neighbor query based on approximate keywords
  • false positive rate false detection rate
  • KNN algorithm Proximity algorithm, is one of the simplest methods in data mining classification technology.
  • I-Node A leaf R-tree node, which stores an inverted list that maps each keyword to a spatial keyword object.
  • the technical problem to be solved by the present invention is to provide an approximate search method for spatial keyword query applied to electronic maps, which can avoid unnecessary text matching processes, reduce matching costs, and achieve better search results.
  • An approximate search method for spatial keyword query applied to electronic maps which includes the following steps: S1: read each piece of data in a data set to construct an index, and jump to step S2 for each keyword of a single piece of data; S2 : Compare the frequency of the keyword with the frequency threshold. If the frequency value is higher than the preset frequency threshold, skip to S3; if the frequency is lower than the preset frequency threshold, skip to S4; S3 : Use the mapping function to map keywords to R-tree, and organize the space text objects containing the keywords, and jump to S5; S4: Map keywords to the object list, jump to S5; S5: For leaf nodes Calculate the minimum bounding rectangle to represent their spatial area.
  • Step S5 includes the following steps: S61: For non-leaf nodes, calculate the distance from the query point to the MBR of the node, and check whether it matches The given circular area intersects; S62: Select possible child nodes based on the pruning ability of string matching, and visit the corresponding list or R-tree when reaching the leaf node to find the space text object within the given range from the given point ; S63: Obtain the current candidate results, and take the intersection of the temporary results of each keyword as the final solution set.
  • step S7 includes the following steps: S71: read the number of keywords to be queried, if the keyword is a single keyword, jump to S72 , If the keyword is multiple keywords, skip to S73; S72: find the global kNN set by combining the k nearest neighbors corresponding to each string similar to the keyword; S73: extend the LBAK tree to support approximate keys The k nearest neighbor query for word inclusion.
  • the present invention combines the pruning capabilities of spatial matching and text matching in a hybrid index mode, thereby avoiding many unnecessary text matching processes, reducing matching costs, redundant storage of strings, and reducing Cost of storage space.
  • FIG. 1 is a schematic diagram of the working process of Embodiment 1.
  • Figure 2 is a schematic diagram of the influence of the query percentage of the present invention on the query area of the solution when the query percentage gradually increases;
  • Figure 3 is a schematic diagram of the influence of the number of keywords in the present invention on the query area of the scheme when the number of keywords gradually increases.
  • An approximate search method for spatial keyword query applied to electronic maps which includes the following steps:
  • step S1 Read each piece of data in the data set for index construction, and jump to step S2 for each keyword of a single piece of data;
  • S2 Compare the frequency of the keyword with the frequency threshold. If the frequency value is higher than the preset frequency threshold, skip to S3; if the frequency value is lower than the preset frequency threshold, skip to S4;
  • S3 Use the mapping function to map the keyword to the R-tree, and organize the space text objects containing the keyword, and jump to S5;
  • S5 For the objects in the leaf nodes, calculate the minimum bounding rectangle to represent their spatial area. For non-leaf nodes, collect the minimum bounding rectangles of its child nodes and form a larger minimum bounding rectangle to represent the space coverage of the node ;
  • S7 Query algorithm for k nearest neighbor queries contained in approximate keywords: For k nearest neighbor queries contained in approximate keywords with a single keyword, simply find the k nearest neighbors corresponding to each similar string ( Called ⁇ K1,K2,...,Kn ⁇ ). Then find the global kNN set in the union of all possible ⁇ K1,K2,...,Kn ⁇ . For multiple keywords, the situation is more complicated, because the k nearest neighbors of a specific keyword may not contain other query keywords. In this case, the LBAK tree is extended to support k nearest neighbor queries contained in approximate keywords. Since the LBAK tree is similar to BFIR-TREE, the latter's eBKQ algorithm can be quickly modified to support k nearest neighbor queries contained in approximate keywords.
  • mapping function in step S2 has the following attributes:
  • Attribute 1 For a given two strings si and sj, if the size of ⁇ (si) and ⁇ (sj) is compared in a linear time relative to the length of the given string, then the ⁇ function is called efficient Comparison of. Based on attribute 1, we can effectively organize strings based on B+-tree and handle string update operations.
  • Attribute 2 Given a string q and a string interval [si,sj], if the mapping function ⁇ can efficiently return the minimum edit distance between q and the string interval, then ⁇ is called a lower bound deterministic mapping. Bed-Tree stores the minimum and maximum strings smin and smax in all strings at the same time, which represent the boundaries of the strings in the Bed-Tree under a given mapping.
  • the string in the root node n is represented as ⁇ s1,...,sk ⁇ , and its candidate string interval: ⁇ (smin,s1),(s1,s2),...,(sk-1,sk),( sk,smax) ⁇ .
  • the experiment was performed on a cluster of 17 nodes with two configurations: (1) 8 machines with 6-core Intel Xeon E5-2603 v3 1.60GHz processors and 20GB RAM; (2) 2 machines with 6-core Intel Xeon E5-2620 2.00GHz processor and 56GB RAM machine; (3) 7 machines equipped with 6-core Intel Xeon E5-2609 1.90GHz processor and 16GB RAM.
  • Each slave node uses 15GB of memory and all available 6 cores for subsequent calculations. All nodes are running on the Ubuntu 14.04.2 LTS system, with Hadoop 2.4.1 and Spark 1.3.0 installed. Perform related experiments on two real massive data sets.
  • Bed-first-tree When the query area is small, Bed-first-tree is slightly better than LBAK-Tree. When the query area expands, Bed-first-tree is more scalable and stable. As the number of query keywords increases, the performance of both increases first and then continues to decline. Generally, when there is only one query keyword, many results will be retrieved, and when two query keywords are assigned, many nodes become irrelevant solutions, so the number of visits to nodes is greatly reduced, resulting in a reduction in search overhead. However, when the number of query keywords continues to increase, the overhead of string search will become a major problem. Therefore, when the keyword size is greater than 2, its performance shows a downward trend. Therefore, the technical solution of the present invention is suitable for service applications based on geographic location such as public comment.

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Abstract

本发明公开了一种应用于电子地图的空间关键字查询的近似搜索方法，其包括如下步骤：S1：读取数据集的各条数据进行索引构建、针对单条数据的各个关键字分别跳转至步骤S2；S2:将关键字的频率与频率阈值进行比对；S3：采用映射函数将关键字映射到R-tree，并组织包含该关键字的空间文本对象，跳转至S5；S4：将关键字映射到对象列表中，跳转至S5；S5：对于叶子节点中的对象、计算其最小边界矩形来表示它们的空间区域，对于非叶子节点，收集其子节点的最小边界矩形并组成更大的最小边界矩形以代表该节点的空间覆盖范围；S6：在S5构建的空间框架内实现对aBRQ(Approximate Boolean Range Query)的查询搜索；S7：在S5构建的空间框架内实现对近似关键字包含的k最近邻查询的查询搜索。

Description

应用于电子地图的空间关键字查询的近似搜索方法 技术领域
本发明属于定位技术领域，具体来说涉及一种基于Spark平台的应用于电子地图的空间关键字查询的近似搜索方法。
背景技术
近年来随着通信技术的发展和移动终端的广泛使用，基于位置的社会服务层出不穷。空间关键字查询是以用户的地理位置信息和多个查询关键字作为参数，返回和这些参数有着空间和文本相关度的空间对象。所谓近似关键字搜索，是指搜索给定距离度量下，与查询字符串相似的字符串。目前，主流的方案是基于空间优先的索引结构，然而，基于空间优先的结构对于平均具有数十个关键字的空间文本对象进行索引优化并不高效。因此，如何开发出一种基于文本匹配优先的空间关键字查询近似搜索方法，以避免无必要的文本匹配过程，降低匹配开销、以取得更好的搜索效果。是本领域技术人员需要研究的方向。以下为本申请中所涉及的字母缩写的注释：R-tree：B-tree向多维空间发展的另一种形式，它将空间对象按范围划分，每个结点都对应一个区域和一个磁盘页，非叶结点的磁盘页中存储其所有子结点的区域范围，非叶结点的所有子结点的区域都落在它的区域范围之内。IR-tree:以倒排索引和R-tree索引为基础，通过倒排索引解决文本相似度的计算模型。BFIR-tree：基于海量数据处理实现的IR-tree；CBFIR-tree：动态的BFIR-tree；S2I-V结构：对不同频率的关键字应被区别处理的模型结构；eBRQ：基于关键字包含的范围查询；aBRQ：基于近似关键字包含的k最近邻查询；false positive rate：误检率；。KNN算法：即临近算法，是数据挖掘分类技术中最简单的方法之一。I-Node：一个叶子R树节点，它存储了将每个关键字映射到空间关键字对象的倒排列表。
发明内容
本发明要解决的技术问题是提供了一种应用于电子地图的空间关键字查询的近似搜索方法，能够避免无必要的文本匹配过程，降低匹配开销、以取得更好的搜索效果。
其采用的技术方案如下：
一种应用于电子地图的空间关键字查询的近似搜索方法，其包括如下步骤：S1：读取数据集的各条数据进行索引构建、针对单条数据的各个关键字分别跳转至步骤S2；S2:将关键字的频率与频率阈值进行比对，若该频率值高于预设的频率阈值，则跳转至S3、若该 频率值低于预设的频率阈值，则跳转至S4；S3：采用映射函数将关键字映射到R-tree，并组织包含该关键字的空间文本对象，跳转至S5；S4：将关键字映射到对象列表中，跳转至S5；S5：对于叶子节点中的对象、计算其最小边界矩形来表示它们的空间区域，对于非叶子节点，收集其子节点的最小边界矩形并组成更大的最小边界矩形以代表该节点的空间覆盖范围；S6：在S5构建的空间框架内实现对aBRQ的查询搜索；S7：在S5构建的空间框架内实现对近似关键字包含的k最近邻查询的查询搜索。
优选的是，上述应用于电子地图的空间关键字查询的近似搜索方法中：步骤S5包括如下步骤：S61：对于非叶子节点，计算从查询点到该节点的MBR的距离，并检查它是否与给定圆形区域相交；S62：基于字符串匹配的剪枝能力选择可能的子节点，并在到达叶节点时访问相应的列表或R树以查找离给定点在给定范围内的空间文本对象；S63：获取当前的候选结果、对每个关键字的临时结果取交集作为最后解集。
更优选的是，上述应用于电子地图的空间关键字查询的近似搜索方法中，步骤S7包括如下步骤：S71：读取待查询关键字的数目，若关键字为单关键字，跳转至S72，若关键字为多关键字，跳转至S73；S72：将每个与关键字相似的字符串对应的k个最近邻的并集中找到全局的kNN集合；S73：扩展LBAK树以支持近似关键字包含的k最近邻查询。
通过采用上述技术方案：本发明以混合索引的方式将空间匹配和文本匹配的剪枝能力进行结合，从而避免了很多不必要的文本匹配过程，降低匹配开销、字符串的冗余存储，降低了存储空间成本。
附图说明
下面结合附图与具体实施方式对本发明作进一步详细的说明：
图1为实施例1的工作流程示意图。
图2为本发明中查询百分比逐渐增加时对本方案查询区域的影响示意图；
图3为本发明中关键字数目逐渐增加时对本方案查询区域的影响示意图。
具体实施方式
为了更清楚地说明本发明的技术方案，下面将结合各个实施例作进一步描述。
如图1所示为实施例1：
一种应用于电子地图的空间关键字查询的近似搜索方法，其包括如下步骤：
S1：读取数据集的各条数据进行索引构建、针对单条数据的各个关键字分别跳转至步骤S2；
S2：将关键字的频率与频率阈值进行比对，若该频率值高于预设的频率阈值，则跳转至S3、若该频率值低于预设的频率阈值，则跳转至S4；
S3：采用映射函数将关键字映射到R-tree，并组织包含该关键字的空间文本对象，跳转至S5；
S4：将关键字映射到对象列表中，跳转至S5；
S5：对于叶子节点中的对象、计算其最小边界矩形来表示它们的空间区域，对于非叶子节点，收集其子节点的最小边界矩形并组成更大的最小边界矩形以代表该节点的空间覆盖范围；
S6：对aBRQ的查询算法：假设一个aBRQ Q＝{Qs＝(τ,ε),Qt＝{(s1,h1),...,(sn,hn)}}，对于每个关键字访问一次Bed-first-tree(算法1行2)。在访问Bed-first-tree的时候，对于非叶子节点，首先计算从查询点到该节点的MBR的距离，并检查它是否与给定圆形区域相交。然后利用字符串匹配的剪枝能力来选择可能的子节点。如果到达叶节点，访问相应的列表或R树以查找离给定点在给定范围内的空间文本对象，然后获得当前的候选结果。对每个关键字的临时结果取交集既可获得最后的解集；
S7：对近似关键字包含的k最近邻查询的查询算法：对于具有单个关键字的近似关键字包含的k最近邻查询，简单地找到为每个与其相似的字符串对应的k个最近邻(称为{K1,K2,…,Kn})。然后在所有可能的{K1,K2,…,Kn}的并集中找到全局的kNN集合。对于多个关键字，情况则比较复杂，因为特定关键字的k最近邻可能不包含其他的查询关键字。在这种情况下，扩展LBAK树以支持近似关键字包含的k最近邻查询。由于LBAK树与BFIR-TREE类似，从而可以快速修改后者的eBKQ算法来支持近似关键字包含的k最近邻查询。
在上述实施例中：步骤S2中所述映射函数具有以下属性：
属性1：对于给定的2个字符串si和sj，如在相对于给定的字符串长度为线性的时间内比较φ(si)和φ(sj)的大小，则称φ函数为可高效比较的。基于属性1，我们可以基于B+-tree有效的对字符串进行组织，处理字符串更新操作。
属性2：给定字符串q和字符串区间[si,sj]，如果映射函数φ可以高效地返回q和字符串区间的最小编辑距离，则称φ为下界确定性映射。Bed-Tree同时存储了所有字符串中的最小和最大字符串smin和smax，它们表示在给定映射下Bed-Tree中字符串的边界。在根节点n中的字符串表示为{s1,…,sk}，其候选字符串区间：{(smin,s1),(s1,s2),...,(sk-1,sk),(sk,smax)}。通过计算每个字符串区间到给定查询字符串的编辑距离下界，我们可以排除不可能成为最终解的子节点。对于幸存的字符串区间，我们访问相应的子节点， 并递归地遍历Bed-Tree直到到达叶子节点，在叶子节点，我们将计算查询关键字和叶子节点中的各个字符串之间的编辑距离，以验证候选者得到最终解。
比对实验
实验在具有两种配置的由17个节点组成的集群上执行：(1)8台具有6核Intel Xeon E5-2603 v3 1.60GHz处理器和20GB RAM的机器；(2)2台配备6核Intel Xeon E5-2620 2.00GHz处理器和56GB RAM的机器；(3)7台配备6核Intel Xeon E5-2609 1.90GHz处理器和16GB RAM的机器。我们选择一台类型(2)的机器作为主节点，其他机器作为从节点。每个从节点使用15GB内存和所有可用的6个内核进行后续计算。所有节点在Ubuntu 14.04.2 LTS系统上运行，并安装有Hadoop 2.4.1和Spark 1.3.0。在两个真实的海量数据集上进行相关实验。
如图2-3所示：
当查询区域较小时，Bed-first-tree稍微优于LBAK-Tree。而当查询区域扩大时，Bed-first-tree更具可扩展性和稳定性。随着查询关键词数量的增加，两者的表现都先增长，然后持续下降。通常，当只有一个查询关键字时，会检索出许多结果，而当分配两个查询关键字时，很多节点成为无关解，因此访问节点的次数大大减少，导致搜索开销的降低。然而，当查询关键字的数量继续增加时，字符串搜索的开销将成为主要问题。因此，当关键字大小大于2时，其性能出现下降趋势。因此，本发明的技术方案适用于大众点评等基于地理位置的服务应用。
以上所述，仅为本发明的具体实施例，但本发明的保护范围并不局限于此，任何熟悉本领域技术的技术人员在本发明公开的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。本发明的保护范围以权利要求书的保护范围为准。

Claims (3)

  1. 一种应用于电子地图的空间关键字查询的近似搜索方法，其特征在于，包括如下步骤：
    S1：读取数据集的各条数据进行索引构建、针对单条数据的各个关键字分别跳转至步骤S2；
    S2:将关键字的频率与频率阈值进行比对，若该频率值高于预设的频率阈值，则跳转至S3、若该频率值低于预设的频率阈值，则跳转至S4；
    S3：采用映射函数将关键字映射到R-tree，并组织包含该关键字的空间文本对象，跳转至S5；
    S4：将关键字映射到对象列表中，跳转至S5；
    S5：对于叶子节点中的对象、计算其最小边界矩形来表示它们的空间区域，对于非叶子节点，收集其子节点的最小边界矩形并组成更大的最小边界矩形以代表该节点的空间覆盖范围；
    S6：在S5构建的空间框架内实现对基于近似关键字包含的k个最近邻查询的查询搜索；
    S7：在S5构建的空间框架内实现对近似关键字包含的k最近邻查询的查询搜索。
  2. 如权利要求1所述应用于电子地图的空间关键字查询的近似搜索方法，其特征在于，步骤S6包括如下步骤：
    S61：对于非叶子节点，计算从查询点到该节点的MBR的距离，并检查它是否与给定圆形区域相交；
    S62：基于字符串匹配的剪枝能力选择可能的子节点，并在到达叶节点时访问相应的列表或R树以查找离给定点在给定范围内的空间文本对象；
    S63：获取当前的候选结果、对每个关键字的临时结果取交集作为最后解集。
  3. 如权利要求1所述应用于电子地图的空间关键字查询的近似搜索方法，其特征在于，步骤S7包括如下步骤：
    S71：读取待查询关键字的数目，若关键字为单关键字，跳转至S72，若关键字为多关键字，跳转至S73；
    S72：将每个与关键字相似的字符串对应的k个最近邻的并集中找到全局的kNN集合；
    S73：扩展LBAK树以支持近似关键字包含的k最近邻查询。
PCT/CN2019/088771 2019-04-24 2019-05-28 应用于电子地图的空间关键字查询的近似搜索方法 Ceased WO2020215437A1 (zh)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022161368A1 (zh) * 2021-01-27 2022-08-04 京东城市(北京)数字科技有限公司 空间二元组的确定方法、装置、计算机设备和存储介质
CN116303434A (zh) * 2023-02-20 2023-06-23 厦门亿力吉奥信息科技有限公司 基于网格r树混合索引构建方法、检索方法和装置
CN121502033A (zh) * 2026-01-14 2026-02-10 烟台大学 一种基于距离约束的视频时空量化范围检索方法及系统

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113849498B (zh) * 2021-08-18 2022-08-23 北京邮电大学 一种索引构建及查询方法
CN113779039A (zh) * 2021-09-26 2021-12-10 辽宁工程技术大学 Top-k集合空间关键字近似查询方法
CN113934944B (zh) * 2021-10-13 2025-01-24 深圳大学 一种面向空间文本数据的空间结构匹配方法
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090037403A1 (en) * 2007-07-31 2009-02-05 Microsoft Corporation Generalized location identification
CN102214215A (zh) * 2011-06-07 2011-10-12 陆嘉恒 一种基于文本信息的快速反近邻查询方法
CN104376112A (zh) * 2014-11-27 2015-02-25 苏州大学 一种路网上空间关键字检索的方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090037403A1 (en) * 2007-07-31 2009-02-05 Microsoft Corporation Generalized location identification
CN102214215A (zh) * 2011-06-07 2011-10-12 陆嘉恒 一种基于文本信息的快速反近邻查询方法
CN104376112A (zh) * 2014-11-27 2015-02-25 苏州大学 一种路网上空间关键字检索的方法

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022161368A1 (zh) * 2021-01-27 2022-08-04 京东城市(北京)数字科技有限公司 空间二元组的确定方法、装置、计算机设备和存储介质
CN116303434A (zh) * 2023-02-20 2023-06-23 厦门亿力吉奥信息科技有限公司 基于网格r树混合索引构建方法、检索方法和装置
CN121502033A (zh) * 2026-01-14 2026-02-10 烟台大学 一种基于距离约束的视频时空量化范围检索方法及系统

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