University of Bahrain
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Using model Driven Engineering to transform Big Data query languages to MapReduce jobs

Show simple item record Erraissi, Allae 2020-07-21T13:55:32Z 2020-07-21T13:55:32Z 2020-07-01
dc.identifier.issn 2210-142X
dc.description.abstract Big Data processing is done using MapReduce which is a clustered data processing framework. Composed of Map and Reduce functions, it distributes data processing tasks between different computers, then reduces the results in a single summary. Most data analysts prefer to use query languages like Pig and Hive to process Big Data, given the complexity of the MapReduce paradigm. In this paper, we propose an approach based on Model Engineering to transform requests written by Pig or Hive to MapReduce jobs thanks to the use of the ATL transformation language. Our proposal will allow us to easily obtain MapReduce programs from requests written in Pig or Hive. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject MapReduce en_US
dc.subject Model Driven Engineering en_US
dc.subject Hive en_US
dc.subject Pig en_US
dc.title Using model Driven Engineering to transform Big Data query languages to MapReduce jobs en_US
dc.type Article en_US
dc.volume 9 en_US
dc.pagestart 1 en_US
dc.pageend 9 en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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