Processing theta-joins on shared-nothing systems

Processing theta-joins on shared-nothing systems

Joins are essential for many large-scale data analysis tasks, and a variety of join conditions must be supported for many applications such as data-driven science, advertising, marketing, and social networks. Efficient parallel execution of joins is crucial to cope with the large volumes of data bei...

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Online Access:http://hdl.handle.net/2047/d20005009
id ndltd-NEU--neu-336346
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spelling ndltd-NEU--neu-3363462021-05-26T05:09:49ZProcessing theta-joins on shared-nothing systemsJoins are essential for many large-scale data analysis tasks, and a variety of join conditions must be supported for many applications such as data-driven science, advertising, marketing, and social networks. Efficient parallel execution of joins is crucial to cope with the large volumes of data being collected and generated in many disciplines.http://hdl.handle.net/2047/d20005009
collection NDLTD
sources NDLTD
description Joins are essential for many large-scale data analysis tasks, and a variety of join conditions must be supported for many applications such as data-driven science, advertising, marketing, and social networks. Efficient parallel execution of joins is crucial to cope with the large volumes of data being collected and generated in many disciplines.
title Processing theta-joins on shared-nothing systems
spellingShingle Processing theta-joins on shared-nothing systems
title_short Processing theta-joins on shared-nothing systems
title_full Processing theta-joins on shared-nothing systems
title_fullStr Processing theta-joins on shared-nothing systems
title_full_unstemmed Processing theta-joins on shared-nothing systems
title_sort processing theta-joins on shared-nothing systems
publishDate
url http://hdl.handle.net/2047/d20005009
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