Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework
10.1145/1989323.1989424
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sg-nus-scholar.10635-405532024-11-09T01:55:09Z Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework Lin, Y. Agrawal, D. Chen, C. Ooi, B.C. Wu, S. COMPUTER SCIENCE column store join MapReduce 10.1145/1989323.1989424 Proceedings of the ACM SIGMOD International Conference on Management of Data 961-972 2013-07-04T08:06:59Z 2013-07-04T08:06:59Z 2011 Conference Paper Lin, Y.,Agrawal, D.,Chen, C.,Ooi, B.C.,Wu, S. (2011). Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework. Proceedings of the ACM SIGMOD International Conference on Management of Data : 961-972. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1989323.1989424" target="_blank">https://doi.org/10.1145/1989323.1989424</a> 9781450306614 07308078 http://scholarbank.nus.edu.sg/handle/10635/40553 NOT_IN_WOS Scopus |
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column store join MapReduce Lin, Y. Agrawal, D. Chen, C. Ooi, B.C. Wu, S. Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework |
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10.1145/1989323.1989424 |
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COMPUTER SCIENCE |
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COMPUTER SCIENCE Lin, Y. Agrawal, D. Chen, C. Ooi, B.C. Wu, S. |
format |
Conference or Workshop Item |
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Lin, Y. Agrawal, D. Chen, C. Ooi, B.C. Wu, S. |
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Lin, Y. |
title |
Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework |
title_short |
Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework |
title_full |
Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework |
title_fullStr |
Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework |
title_full_unstemmed |
Llama: Leveraging columnar storage for scalable join processing in the MapReduce framework |
title_sort |
llama: leveraging columnar storage for scalable join processing in the mapreduce framework |
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2013 |
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http://scholarbank.nus.edu.sg/handle/10635/40553 |
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1821198033202708480 |