Main Reference PaperContent-aware Partial Compression for Textual Big Data Analysis in Hadoop, IEEE Transactions on Big Data, 2018 [Java/Hadoop]
  • Compression is an effective way of reducing data size. This work proposes the compression techniques called the Record-aware Partial Compression (RaPC) scheme, which is suitable for textual big data analysis in Hadoop. In RaPC scheme, each layer is designed for the corresponding stage of data processing. The design goal of the outer layer compression scheme is to maximally reduce the data size for minimizing data loading time while making the compressed data splittable to the HDFS.

Description
  • Compression is an effective way of reducing data size. This work proposes the compression techniques called the Record-aware Partial Compression (RaPC) scheme, which is suitable for textual big data analysis in Hadoop. In RaPC scheme, each layer is designed for the corresponding stage of data processing. The design goal of the outer layer compression scheme is to maximally reduce the data size for minimizing data loading time while making the compressed data splittable to the HDFS.

  • To reduce the input data size.

  • Reducing Execution Time for compressing the big data.

Aim & Objectives
  • To reduce the input data size.

  • Reducing Execution Time for compressing the big data.

  • A technique is contributed to improve the scalability of IoT data.

Contribution
  • A technique is contributed to improve the scalability of IoT data.

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Project Recommended For
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Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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