An Effective Compact Representation Model for Big Sparse Matrix Multiplication

  • Authors

    • G. Somasekhar
    • K. Karthikeyan
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.20827
  • Big data, Hadoop, Mapreduce, Matrix multiplication, Sparse data, Sparse matrix.

  • Abstract

    The importance of big data is increasing day by day motivating the researchers towards new inventions. Often, a small amount of data is needed to achieve a solution or to draw a conclusion. The new big data techniques stem from the necessity to retrieve, store and process the required data out of huge data volumes. The present paper focuses on dealing with sparse matrices which is fre-quently needed in many sparse big data applications nowadays. It applies compact representation techniques of sparse data and moulds the required data in the mapreducible format. Then the mapreduce strategy is used to get the results quickly which saves execution time and improves scalability. Finally we established that the new algorithm performs well in sparse big data scenario compared to the existing techniques in big data processing.

     

     

  • References

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  • How to Cite

    Somasekhar, G., & Karthikeyan, K. (2018). An Effective Compact Representation Model for Big Sparse Matrix Multiplication. International Journal of Engineering & Technology, 7(4.10), 157-162. https://doi.org/10.14419/ijet.v7i4.10.20827

    Received date: 2018-10-03

    Accepted date: 2018-10-03

    Published date: 2018-10-02