IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v12y2020i4p386-414.html
   My bibliography  Save this article

Data modelling for large-scale social media analytics: design challenges and lessons learned

Author

Listed:
  • Ahmet Arif Aydin
  • Kenneth M. Anderson

Abstract

We live in a world of big data; organisations collect, store, and analyse large volumes of data for various purposes. The five V's of big data introduce new challenges for developers to handle when performing data processing and analysis. Indeed, data modelling is one of the most challenging and critical aspects of big data because it determines how data will be structured and stored; these decisions then impact how that data can be processed and analysed. In this paper, we report on designing a data model for storing and analysing Twitter data in support of crisis informatics. In this work, we leverage the data model provided by columnar NoSQL data stores to design column families that can efficiently index, sort, store and analyse large Twitter datasets. In particular, our column families are designed to achieve efficient batch data processing. We evaluate these claims and discuss our future work.

Suggested Citation

  • Ahmet Arif Aydin & Kenneth M. Anderson, 2020. "Data modelling for large-scale social media analytics: design challenges and lessons learned," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 12(4), pages 386-414.
    • Handle: RePEc:ids:ijdmmm:v:12:y:2020:i:4:p:386-414
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=111409
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijdmmm:v:12:y:2020:i:4:p:386-414. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    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.