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MASSIVE DATA PROCESSING USING MAPREDUCE AGGREGATION TO MAKE DIGITIZED INDIA

机译:使用MapReduce聚合来制作数字化印度的大规模数据处理

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Digitized India is used to connect rural areas with high speed Internet. As a result, it is used to reduce crime, manual power, documentation and also increases the job opportunities. Nowadays people are facing many problems when they forget to carry the driving license and also to reduce the corruption, the proposed system combines the driving license with Aadhar card. The details of driving license and Aadhar card data can be combined using the MapReduce Counters. It automatically aggregated over Map and Reduce phases. It is used to create a tool that manages the handling of license using unique identification associated with each individual. It helps the user to travel various places without having the license. So the proposed system will make the digitization of data on a large scale for easy and quick access throughout the India. Sqoop is a tool intended to exchange information amongst Hadoop and social databases. Sqoop utilizes MapReduce to import and export the information, which gives parallel operation and in addition adaptation to non-critical failure. As the result of parallel operations time utilization for transferring the data get decreased radically.
机译:数字化印度用于连接高速互联网的农村地区。结果,它用于减少犯罪,手动权力,文档,并增加就业机会。如今人们在忘记携带驾驶执照时面临着许多问题,也将拟议的系统结合了与Aadhar卡的驾驶执照相结合。可以使用MapReduce计数器组合驾驶许可证和Aadhar卡数据的细节。它会在地图上自动聚合并减少阶段。它用于创建一个工具,该工具使用与每个单独的唯一标识进行唯一标识来管理许可证的处理。它帮助用户在没有许可证的情况下旅行各个地方。因此,所提出的系统将以大规模的数据进行数字化,以便在整个印度轻松获得。 SQOOP是一个旨在在Hadoop和社交数据库之间交换信息的工具。 SQOOM利用MapReduce导入和导出信息,它给出了并行操作和添加到非严重失败的适应。由于并行操作的结果,用于传输数据的时间利用率从根本上缩短。

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