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• W2894867913 abstract "A limited resource processing platform may not be suited to process a large volume of data. The distributed processing platforms can solve this problem by incorporating commodity hardware collaboratively to process a large volume of data. The MapReduce programming framework is one candidate framework for large-scale processing, and Hadoop is its open-source implementation. This framework consists of the Hadoop Distributed File System and the MapReduce for computation capabilities. However, the MapReduce framework does not allow for data sharing for computation among the computing nodes. In this paper, we present an implementation of a sliding-window algorithm for data sharing for computation dependency in MapReduce. The algorithm is designed to facilitate the data processing a sequential order, e.g., moving average. The algorithm utilizes the MapReduce job metadata, e.g., input split size, to prepare the shared data between the computing nodes without violating the MapReduce fault tolerance handling mechanism." @default.
• W2894867913 created "2018-10-12" @default.
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• W2894867913 date "2018-01-01" @default.
• W2894867913 modified "2023-09-24" @default.
• W2894867913 title "A Sliding-Window Algorithm Implementation in MapReduce" @default.
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• W2894867913 doi "https://doi.org/10.1007/978-3-319-95810-1_6" @default.
• W2894867913 hasPublicationYear "2018" @default.
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