A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
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 proposeMGSJoin, a scalable algorithm followingdoi:10.1155/2014/749028 pmid:25121135 pmcid:PMC4121100 fatcat:w66vd5lfjbg5tfdmimlnsji4qu