Efficient Parallel Set-Similarity Joins Using MapReduce
Department of Computer Science University of California, Irvine Abstract In this paper we study how to efficiently perform set-similarity joins in parallel using the popular MapReduce framework. We propose a 3-stage approach for end-to-end set-similarity joins. We take as input a set of records and output a set of joined records based on a set-similarity condition. We efficiently partition the dat
MapReduce: A Flexible Data Processing Tool – Communications of the ACM
MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs. Mapreduce is a programming model for processing and generating large data sets.4 Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs and a reduce function that merges all intermediate values associated with the sa
Welcome to YSmart's Homepage!
The homepage of YSmartOverview YSmart is a correlation aware SQL-to-MapReduce translator, which is built on top of the Hadoop platform. For a given SQL query and related table schemas, YSmart can automatically translate the query into a series of Hadoop MapReduce programs written in Java. Compared to other SQL-to-MapReduce translators, YSmart has been proved to have the following advantages: High
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Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) | atbrox
Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) The prior update of this posting was in May, and a lot has happened related to Mapreduce and Hadoop since then, e.g. 1) big software companies have started offering hadoop-based software (Microsoft and Oracle), 2) Hadoop-startups have raised record amounts, and 3) nosql-landscape becoming increasingly datawarehouse’ish and sq
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