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Efficient and scalable graph similarity joins in MapReduce.

Chen Y, Zhao X, Xiao C, Zhang W, Tang J - ScientificWorldJournal (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Information System and Management, National University of Defense Technology, Changsha 410073, China.

ABSTRACT
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.

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MGSJoin.
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alg1: MGSJoin.

Mentions: In the filtering phase of Algorithm 1, two MapReduce jobs are required, with many intermediate key-value pairs generated and transmitted. These increase the I/O and communication cost, which can be fairly time-consuming. This section introduces the Bloom filter technique to reduce such cost. Next, we first recall the concept of spectral Bloom filters.


Efficient and scalable graph similarity joins in MapReduce.

Chen Y, Zhao X, Xiao C, Zhang W, Tang J - ScientificWorldJournal (2014)

MGSJoin.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4121100&req=5

alg1: MGSJoin.
Mentions: In the filtering phase of Algorithm 1, two MapReduce jobs are required, with many intermediate key-value pairs generated and transmitted. These increase the I/O and communication cost, which can be fairly time-consuming. This section introduces the Bloom filter technique to reduce such cost. Next, we first recall the concept of spectral Bloom filters.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Information System and Management, National University of Defense Technology, Changsha 410073, China.

ABSTRACT
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.

Show MeSH