.png){kind=icon}
返回
VChunkJoin: An Efficient Algorithm for Edit Similarity Joins
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2012.79.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Similarity joins play an important role in many application areas, such as data integration and cleaning, record linkage, and pattern recognition. In this paper, we study efficient algorithms for similarity joins with an edit distance constraint. Currently, the most prevalent approach is based on extracting overlapping grams from strings and considering only strings that share a certain number of grams as candidates. Unlike these existing approaches, we propose a novel approach to edit similarity join based on extracting nonoverlapping substrings, or chunks, from strings. We propose a class of chunking schemes based on the notion of tail-restricted chunk boundary dictionary. A new algorithm, VChunkJoin, is designed by integrating existing filtering methods and several new filters unique to our chunk-based method. We also design a greedy algorithm to automatically select a good chunking scheme for a given data set. We demonstrate experimentally that the new algorithm is faster than alternative methods yet occupies less space.
Keyword:
Edit similarity joins
approximate string matching
prefix filtering
q-gram
edit distance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
分享收藏Which way does water flow? An econometric analysis of the global price integration of water stocks
OAAI
OAAI分享收藏THE EFFECTS OF PLANNING ON FLUENCY, COMPLEXITY, AND ACCURACY IN SECOND LANGUAGE NARRATIVE WRITING
PREAI
分享收藏
查看更多内容