IDEAS home Printed from https://ideas.repec.org/a/ijs/ijsrse/v4y2018i6idhijsrset1848154.html

Big Data Processing with Data Provenance Using HDM Framework

Author

Listed:
  • Rajat Bodankar
  • Roshani Talmale

Abstract

Big Data applications are becoming more complex and expe-riencing frequent changes and updates. In practice, manual optimization of complex big data jobs is time-consuming and error-prone. Maintenance and management of evolving big data applications is a challenging task as well. We demon-strate HDM, Hierarchically Distributed Data Matrix, as a big data processing framework with built-in data ow op-timizations and integrated maintenance of data provenance information that supports the management of continuously evolving big data applications. In HDM, the data ow of jobs are automatically optimized based on the functional DAG representation to improve the performance during ex-ecution. Additionally, comprehensive meta-data related to explanation, execution and dependency updates of HDM ap-plications are stored and maintained in order to facilitate the debugging, monitoring, tracing and reproducing of HDM jobs and programs.

Suggested Citation

  • Rajat Bodankar & Roshani Talmale, 2018. "Big Data Processing with Data Provenance Using HDM Framework," International Journal of Scientific Research in Science, Engineering and Technology, International Journal of Scientific Research in Science, Engineering and Technology, vol. 4(6), pages 210-214, February.
    • Handle: RePEc:ijs:ijsrse:v4:y2018:i6:id:hijsrset1848154
    Note: Article URL: https://ijsrset.com/IJSRSET1848154
    as

    Download full text from publisher

    File URL: https://ijsrset.com/IJSRSET1848154
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrset.com/paper/4913.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ijs:ijsrse:v4:y2018:i6:id:hijsrset1848154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (email available below). General contact details of provider: https://ijsrset.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.