• DocumentCode
    2368250
  • Title

    Executing multiple group-by query in a MapReduce approach

  • Author

    Pan, Jie ; Magoulès, Frédéric ; Le Biannic, Yann

  • Author_Institution
    Ecole Centrale Paris, Châtenay-Malabry, France
  • Volume
    2
  • fYear
    2010
  • fDate
    June 29 2010-July 1 2010
  • Firstpage
    38
  • Lastpage
    41
  • Abstract
    Facing more and more generated information, data analysis software meets the challenge of processing large volume of data. The arrival of MapReduce provides a chance to utilize commodity hardware for processing large data set in parallel. In this paper, we focus on a special type of data analysis query, namely, multiple group-by query. We give an initial implementation of multiple group-by query based on MapReduce model. Considering the ignorable communication cost, we then propose an optimized version based on MapCombineReduce model, which addresses this issue. Our optimized version shows a better accelerating ability and a better scalability than the initial version.
  • Keywords
    data analysis; parallel processing; query processing; very large databases; MapCombineReduce model; commodity hardware; communication cost; data analysis software; group-by query; large volume data processing; parallel processing; Analytical models; Biological system modeling; Computational modeling; Monitoring; MapCombineReduce; MapReduce; multiple group by query;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Systems, Networks and Applications (ICCSNA), 2010 Second International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-7475-2
  • Type

    conf

  • DOI
    10.1109/ICCSNA.2010.5588949
  • Filename
    5588949