• DocumentCode
    249459
  • Title

    Data Sharing for Cloud Computing Platforms

  • Author

    Sabbouh, Marwan ; McCracken, Kenneth ; Cooney, Geoff

  • Author_Institution
    Social Platform Here, Nokia Bus. Cambridge, Cambridge, MA, USA
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    621
  • Lastpage
    628
  • Abstract
    Cloud computing platforms consist of a set of reliable services that are run in the cloud. Typically, consumer applications use software development kits (SDKs) provided by the computing platform services to store, update, and retrieve instances of data in the cloud. Services provided by the cloud computing platform, expose different data modeling paradigms that consumer applications use to interact with the cloud. The service-specific data modeling paradigms and SDKs increase the complexity of data sharing between consumer applications that interact with the different services of the cloud computing platform. To make matters more complicated, it´s not uncommon in an enterprise to find different groups using different cloud computing platforms. In this paper, we will describe a set of abstractions that can be used to abstract different computing platforms. The abstractions not only abstract the computing platform, but also enable the data discovery and sharing between applications. We will further show that these abstractions do not add substantial latency on the performance of the computing platform.
  • Keywords
    cloud computing; data analysis; data models; SDK; cloud computing platforms; computing platform services; consumer applications; data abstractions; data discovery; data instances; data retrieval; data sharing; data storage; data update; service-specific data modeling paradigms; software development kits; Computational modeling; Data models; Distributed databases; Electronics packaging; OWL; Protocols; Resource description framework; API; JSON; NoSQL; Rest; data modeling language; web protocols;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.95
  • Filename
    6906837