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  Published Paper Details:

  Paper Title

DISTRIBUTED DATA CUBE MATERIALIZATION MECHANISM

  Authors

  RAJIV KUMAR,  Dr S.Venkatanarayanan

  Keywords

Data cube, Multidimensional array, MapReduce Merge, OLAP

  Abstract


Data Cube provides basic abstraction for multi-dimensional data analysis and allows you to discover useful information from a range of data. Online Analytical Processing (OLAP) has taken it to the next level by supporting online responses to analytical queries with the underlying technique that pre-calculates (realizes) the data cube. The data cube materialization is important to OLAP, but a costly task in terms of data processing and storage. It is important that a distributed data cube materialization mechanism be suitable for multidimensional data analysis across the current distributed storage systems. Several research papers are currently available that have considered MapReduce for the data cube materialization. In addition, Apache Spark recently enabled the CUBE operator as part of its DataFrame application.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT1802562

  Paper ID - 183326

  Page Number(s) - 934-940

  Pubished in - Volume 6 | Issue 1 | March 2018

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  RAJIV KUMAR,  Dr S.Venkatanarayanan,   "DISTRIBUTED DATA CUBE MATERIALIZATION MECHANISM", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, pp.934-940, March 2018, Available at :http://www.ijcrt.org/papers/IJCRT1802562.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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