IDEAS home Printed from https://ideas.repec.org/a/igg/jcac00/v8y2018i3p48-59.html
   My bibliography  Save this article

Performance Evaluation of Unstructured PBRA for Bigdata with Cassandra and MongoDB in Cloud

Author

Listed:
  • Sangeeta Gupta

    (Vardhaman College of Engineering, Hyderabad, India)

Abstract

In this article, performance evaluation of web collection data in data stores, such as NoSQL-Cassandra and MongoDB is presented, yielding scalability of applications. In addition to scalability, security of NoSQL databases remains highly unproved. It is noteworthy that existing works in the area of cloud with NoSQL focus on either scalability or security but not both aspects. Also, security, if provided, is at minor interface levels. In this article, the PBRA system is designed to deal with highly unstructured big data emerging from the twitter social networking service, which is new of its kind to strengthen the bigdata security. PBRA is Passphrase Based REST API model where the REST API methods are integrated with the user generated passphrase in addition to the private key for a set of records of user desirable number before storing into the Cassandra and MongoDB databases. Results are presented to illustrate the same for nearly 1 million records and the efficiency of Cassandra over MongoDB is observed. It is observed from the results that though the time taken to load and retrieve bulk data records is higher than dealing with cipher text, Cassandra performs better than MongoDB with the proposed security model.

Suggested Citation

  • Sangeeta Gupta, 2018. "Performance Evaluation of Unstructured PBRA for Bigdata with Cassandra and MongoDB in Cloud," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 8(3), pages 48-59, July.
    • Handle: RePEc:igg:jcac00:v:8:y:2018:i:3:p:48-59
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.2018070104
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jcac00:v:8:y:2018:i:3:p:48-59. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.