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- W2425037143 abstract "MapReduce is a popular framework for analyzing massive datasets. However, MapReduce typically operates on static data by scheduling batch jobs so that it is difficult to process continuous unbounded streams of data in real time. To address this problem, we propose a scalable distributed method, called SDRT-MR, based on MapReduce framework. We first present a memory caching mechanism based on Hash B+ tree, which can be utilized to optimize the high concurrency access performance for intermediate results. To perform incremental one-pass analytics, we further employ dynamic incremental hash technique to enable fast in-memory processing, and provide a frequent keys discovering algorithm to keep hot keys in memory. The experimental results demonstrate that this method can maintain better real-time performance and scalability." @default.
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- W2425037143 date "2016-05-31" @default.
- W2425037143 modified "2023-10-18" @default.
- W2425037143 title "Scalable Distributed Real-Time Processing for Large-Scale Data Streams" @default.
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- W2425037143 doi "https://doi.org/10.14257/ijdta.2016.9.5.06" @default.
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