# A unified framework for string similarity search with edit-distance constraint

A unified framework for string similarity search with edit-distance constraint String similarity search is a fundamental operation in data cleaning and integration. It has two variants: threshold-based string similarity search and top- $$k$$ k string similarity search. Existing algorithms are efficient for either the former or the latter; most of them cannot support both two variants. To address this limitation, we propose a unified framework. We first recursively partition strings into disjoint segments and build a hierarchical segment tree index ( $${\textsf {HS}}{\text {-}}{\textsf {Tree}}$$ HS - Tree ) on top of the segments. Then, we utilize the $${\textsf {HS}}{\text {-}}{\textsf {Tree}}$$ HS - Tree to support similarity search. For threshold-based search, we identify appropriate tree nodes based on the threshold to answer the query and devise an efficient algorithm (HS-Search). For top- $$k$$ k search, we identify promising strings with large possibility to be similar to the query, utilize these strings to estimate an upper bound which is used to prune dissimilar strings and propose an algorithm (HS-Topk). We develop effective pruning techniques to further improve the performance. To support large data sets, we extend our techniques to support the disk-based setting. Experimental results on real-world data sets show that our method achieves high performance on the two problems and outperforms state-of-the-art algorithms by 5–10 times. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

# A unified framework for string similarity search with edit-distance constraint

, Volume 26 (2) – Dec 17, 2016
26 pages

/lp/springer_journal/a-unified-framework-for-string-similarity-search-with-edit-distance-IM7028qdZ0
Publisher
Springer Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-016-0449-y
Publisher site
See Article on Publisher Site

### Abstract

String similarity search is a fundamental operation in data cleaning and integration. It has two variants: threshold-based string similarity search and top- $$k$$ k string similarity search. Existing algorithms are efficient for either the former or the latter; most of them cannot support both two variants. To address this limitation, we propose a unified framework. We first recursively partition strings into disjoint segments and build a hierarchical segment tree index ( $${\textsf {HS}}{\text {-}}{\textsf {Tree}}$$ HS - Tree ) on top of the segments. Then, we utilize the $${\textsf {HS}}{\text {-}}{\textsf {Tree}}$$ HS - Tree to support similarity search. For threshold-based search, we identify appropriate tree nodes based on the threshold to answer the query and devise an efficient algorithm (HS-Search). For top- $$k$$ k search, we identify promising strings with large possibility to be similar to the query, utilize these strings to estimate an upper bound which is used to prune dissimilar strings and propose an algorithm (HS-Topk). We develop effective pruning techniques to further improve the performance. To support large data sets, we extend our techniques to support the disk-based setting. Experimental results on real-world data sets show that our method achieves high performance on the two problems and outperforms state-of-the-art algorithms by 5–10 times.

### Journal

The VLDB JournalSpringer Journals

Published: Dec 17, 2016

## You’re reading a free preview. Subscribe to read the entire article.

### DeepDyve is your personal research library

It’s your single place to instantly
that matters to you.

over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month ### Explore the DeepDyve Library ### Search Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly ### Organize Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place. ### Access Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals. ### Your journals are on DeepDyve Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more. All the latest content is available, no embargo periods. DeepDyve ### Freelancer DeepDyve ### Pro Price FREE$49/month
\$360/year

Save searches from
PubMed

Create lists to

Export lists, citations