Efficient and Scalable Graph Similarity Joins in MapReduce
Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constrai...
| Main Authors: | , , , , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
Hindawi Publishing Corporation
2014
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4121100/ |
| id |
pubmed-4121100 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-41211002014-08-12 Efficient and Scalable Graph Similarity Joins in MapReduce Chen, Yifan Zhao, Xiang Xiao, Chuan Zhang, Weiming Tang, Jiuyang Research Article Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constraints, which return pairs of graphs such that their edit distances are no larger than a given threshold. Leveraging the MapReduce programming model, we propose MGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out nonpromising candidates. With the potential issue of too many key-value pairs in the filtering phase, spectral Bloom filters are introduced to reduce the number of key-value pairs. Furthermore, we integrate the multiway join strategy to boost the verification, where a MapReduce-based method is proposed for GED calculation. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results. Hindawi Publishing Corporation 2014 2014-07-08 /pmc/articles/PMC4121100/ /pubmed/25121135 http://dx.doi.org/10.1155/2014/749028 Text en Copyright © 2014 Yifan Chen et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Chen, Yifan Zhao, Xiang Xiao, Chuan Zhang, Weiming Tang, Jiuyang |
| spellingShingle |
Chen, Yifan Zhao, Xiang Xiao, Chuan Zhang, Weiming Tang, Jiuyang Efficient and Scalable Graph Similarity Joins in MapReduce |
| author_facet |
Chen, Yifan Zhao, Xiang Xiao, Chuan Zhang, Weiming Tang, Jiuyang |
| author_sort |
Chen, Yifan |
| title |
Efficient and Scalable Graph Similarity Joins in MapReduce |
| title_short |
Efficient and Scalable Graph Similarity Joins in MapReduce |
| title_full |
Efficient and Scalable Graph Similarity Joins in MapReduce |
| title_fullStr |
Efficient and Scalable Graph Similarity Joins in MapReduce |
| title_full_unstemmed |
Efficient and Scalable Graph Similarity Joins in MapReduce |
| title_sort |
efficient and scalable graph similarity joins in mapreduce |
| description |
Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constraints, which return pairs of graphs such that their edit distances are no larger than a given threshold. Leveraging the MapReduce programming model, we propose MGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out nonpromising candidates. With the potential issue of too many key-value pairs in the filtering phase, spectral Bloom filters are introduced to reduce the number of key-value pairs. Furthermore, we integrate the multiway join strategy to boost the verification, where a MapReduce-based method is proposed for GED calculation. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4121100/ |
| _version_ |
1613120680524513280 |