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- W2531041707 abstract "With the development of Cloud Computing, the Internet of things and some similar technologies, a large amount of data has been produced. MapReduce as a processing architecture for Cloud Computing has been widely used. It can achieve large-scale data processing. However, when connecting two tables on the data processing model of MapReduce, there will be a great deal of data that do not meet the conditions of the connection. These data will also be transferred from the map side to the reduce side. It will bring more time overhead and I/O cost at shuffle stage, which will result in low efficiency. Therefore, how to improve the join query processing algorithm based on the MapReduce has been an urgent problem. In this paper, we put forward two-table join query processing and optimization strategies for the above problems. The optimized method can achieve the expansion of the Bloom Filter. Meanwhile it can reduce the time of shuffle phase, and improve the efficiency of the system." @default.
- W2531041707 created "2016-10-21" @default.
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- W2531041707 date "2016-01-01" @default.
- W2531041707 modified "2023-09-24" @default.
- W2531041707 title "An Efficient Two-Table Join Query Processing Based on Extended Bloom Filter in MapReduce" @default.
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- W2531041707 doi "https://doi.org/10.1007/978-3-319-47121-1_21" @default.
- W2531041707 hasPublicationYear "2016" @default.
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