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Volume 3 | Issue 10

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Paper Title: Textual BigData Analysis by Record-aware Partial Compression Schema using Hadoop
Authors Name: Manasa Goudashivannavar , Dr.Chetana Prakash
Unique Id: IJRTI1807037
Published In: Volume 3 Issue 7, July-2018
Abstract: As the amount of data is increasing due to the high usage of social media and Internet it is becoming a critical task in analyzing and storage of this semi-structured and unstructured data. The extraction of useful information from these data is widely acknowledged with consequent logical and business exploitation. Continuous increase in the production of data pushes the data analytic platform to their limits. So the effective means for storing the large quantity of data is by using different Compression techniques which is used to reduce data size which has been employed by using many emerging data analytic platforms. The primary motivation behind using compression techniques is to decrease the storage space and transmission cost over the network. Since general purpose compression strategies try to accomplish high level compression proportions which utilizes information change procedures and logical information. The proposed work includes the techniques for more efficient analysis of textual data using record-aware compression schemes which is appropriate for Hadoop platforms and it will be evaluated for number of standard MapReduce tasks by using a collection of private and public datasets.
Keywords: RaPC, Hadoop, BigData, MapReduce
Cite Article: "Textual BigData Analysis by Record-aware Partial Compression Schema using Hadoop", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.3, Issue 7, page no.237 - 240, July-2018, Available :http://www.ijrti.org/papers/IJRTI1807037.pdf
Downloads: 00057
Publication Details: Published Paper ID: IJRTI1807037
Registration ID:180348
Published In: Volume 3 Issue 7, July-2018
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ISSN Number: 2456 - 3315
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