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- W2997461758 abstract "In recent years, to handle huge amount of data, MapReduce Programming Model has been used in parallel which ensures distributed processing. However, data skew is an important issue in MapReduce model which occur when the data are unevenly distributed between the mapper and the reducer which increase the waiting time of the reducer with small sized partition. Several Skew Handling techniques were proposed which tune this process by repartitioning the data into equal sized chunks and distribute them to the reducers. Repartitioning will be suitable for only independent datasets and will degrade the accuracy in dependent datasets. Time Series datasets such as Online Sales, weather and sensor data are dependent data where the prediction of future depends on the historical data. In such case, repartitioning will reduce the prediction accuracy and makes the decision process tedious. A skew handling technique is proposed, where the mapper with huge sized data will be assigned with high-end reducers dynamically." @default.
- W2997461758 created "2020-01-10" @default.
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- W2997461758 date "2020-01-01" @default.
- W2997461758 modified "2023-10-03" @default.
- W2997461758 title "Skew Handling Technique for Scheduling Huge Data Mapper with High End Reducers in MapReduce Programming Model" @default.
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- W2997461758 doi "https://doi.org/10.1007/978-3-030-38501-9_33" @default.
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