Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Approximate String Joins in a Database (Almost) for Free
Proceedings of the 27th International Conference on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
A Primitive Operator for Similarity Joins in Data Cleaning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Map-reduce-merge: simplified relational data processing on large clusters
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
ACM Transactions on Database Systems (TODS)
Efficient parallel set-similarity joins using MapReduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A comparison of join algorithms for log processing in MaPreduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Hadoop: The Definitive Guide
Processing theta-joins using MapReduce
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters
IEEE Transactions on Knowledge and Data Engineering
Exploiting MapReduce-based similarity joins
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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Cloud enabled systems have become a crucial component to efficiently process and analyze massive amounts of data. One of the key data processing and analysis operations is the Similarity Join, which retrieves all data pairs whose distances are smaller than a predefined threshold ε. Even though multiple algorithms and implementation techniques have been proposed for Similarity Joins, very little work has addressed the study of Similarity Joins for cloud systems. This paper focuses on the study, design and implementation techniques of cloud-based Similarity Joins. We present MRSimJoin, a MapReduce based algorithm to efficiently solve the Similarity Join problem. This algorithm efficiently partitions and distributes the data until the subsets are small enough to be processed in a single node. MRSimJoin is general enough to be used with data that lies in any metric space, thus it can be used with multiple data types and distance functions. We present guidelines to implement the algorithm in Hadoop, an open-source cloud system. The experimental evaluation of MRSimJoin shows that it has very good execution time and scalability properties.