频率连接：基于数据划分的一种高效字符串相似性连接算法

骆吉洲; 石胜飞; 王宏志; 李建中

doi:10.1631/FITEE.1601347

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频率连接：基于数据划分的一种高效字符串相似性连接算法

常规文章 | Updated：2022-05-19

- 频率连接：基于数据划分的一种高效字符串相似性连接算法
- FrepJoin: an efficient partition-based algorithm for edit similarity join
- 信息与电子工程前沿（英文） 2017年18卷第10期页码：1499-1510
- Affiliations：
  
  School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
  Guangdong Key Laboratory of Popular High Performance Computers, Key Laboratory of Service Computing and Application, Shenzhen 518000, China
- Author bio：
  
  Ji-zhou LUO, luojizhou@hit.edu.cn
  [ "SHI Sheng-fei, E-mail: shengfei@hit.edu.cn" ]
  [ "WANG Hong-zhi, E-mail: wangzh@hit.edu.cn" ]
  [ "LI Jian-zhong, E-mail: lijzh@hit.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1601347
  中图分类号： TP311.13
- 收稿：2016-06-17，
  
  修回：2017-11-;6，
  
  纸质出版：2017-10
- Accepted：
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骆吉洲, 石胜飞, 王宏志, 等. 频率连接：基于数据划分的一种高效字符串相似性连接算法[J]. 信息与电子工程前沿（英文）, 2017,18(10):1499-1510.

Ji-zhou LUO, Sheng-fei SHI, Hong-zhi WANG, et al. FrepJoin: an efficient partition-based algorithm for edit similarity join[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1499-1510.
骆吉洲, 石胜飞, 王宏志, 等. 频率连接：基于数据划分的一种高效字符串相似性连接算法[J]. 信息与电子工程前沿（英文）, 2017,18(10):1499-1510. DOI： 10.1631/FITEE.1601347.

Ji-zhou LUO, Sheng-fei SHI, Hong-zhi WANG, et al. FrepJoin: an efficient partition-based algorithm for edit similarity join[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1499-1510. DOI： 10.1631/FITEE.1601347.

摘要

字符串相似性连接（string similarity join

SSJ）在很多应用中，特别是在需要找出重复对象的应用中发挥着关键作用。本文关注基于编辑距离的字符串相似性连接。现有算法大多采用先过滤再细化的框架，使得它们很难发现和利用字符串子集间的不相似性，也很难利用如字符频率这样的统计信息。本研究提出了一种基于数据划分的字符串相似性连接算法，它充分利用了这种统计信息。采用频率向量将字符串集划分成一系列较小的子集，使得子集之间的不相似性很容易被发现。本文提出的新算法利用划分后的数据高效地对字符串进行相似性。此外，本文还给出了一个新的过滤器，它能利用字符频率来过滤很多能够通过现有过滤器的不相似字符串。真实数据集上的试验表明，本文提出的算法性能较现有算法有较大幅度的提升。

Abstract

String similarity join (SSJ) is essential for many applications where near-duplicate objects need to be found. This paper targets SSJ with edit distance constraints. The existing algorithms usually adopt the filter-andrefine framework. They cannot catch the dissimilarity between string subsets

and do not fully exploit the statistics such as the frequencies of characters. We investigate to develop a partition-based algorithm by using such statistics. The frequency vectors are used to partition datasets into data chunks with dissimilarity between them being caught easily. A novel algorithm is designed to accelerate SSJ via the partitioned data. A new filter is proposed to leverage the statistics to avoid computing edit distances for a noticeable proportion of candidate pairs which survive the existing filters. Our algorithm outperforms alternative methods notably on real datasets.

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references

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