• DocumentCode
    3006343
  • Title

    Scalable and Trustworthy Cross-Enterprise WfMSs by Cloud Collaboration

  • Author

    Gwan-Hwan Hwang ; Yi-Chan Kao ; Yu-Cheng Hsiao

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Normal Univ., Taipei, Taiwan
  • fYear
    2013
  • fDate
    June 27 2013-July 2 2013
  • Firstpage
    70
  • Lastpage
    77
  • Abstract
    Establishing scalable and cross-enterprise workflow management systems (WfMSs) in the cloud requires the adaptation and extension of existing concepts for process management. This paper proposes a scalable and cross-enterprise WfMS with a multitenancy architecture. Especially, it can activate enactment of workflow processes by cloud collaboration. We do not employ the traditional engine-based WfMSs. The key idea is to have the workflow process instance to be self-protected and does not need a workflow engine to secure the data therein. Thus, the process instance discovery and activity execution can be fully independently and distributed. As a result, we can employ the data storage system, Big Table, to store the process instances, which may form a big data. The applying of element-wise encryption and chained digital signature makes it satisfy major security requirements of authentication, confidentiality, data integrity, and nonrepudiation.
  • Keywords
    cloud computing; cryptography; digital signatures; workflow management software; activity execution; authentication requirement; big table system; chained digital signature; cloud collaboration; confidentiality requirement; cross-enterprise WfMS; data integrity requirement; data storage system; element-wise encryption; multitenancy architecture; nonrepudiation requirement; process instance discovery; process management; security requirement; workflow engine; workflow management system; workflow process enactment; Cloud computing; Digital signatures; Engines; Organizations; Portals; Process control; Servers; Cloud; Multitenancy; WfMS;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2013 IEEE International Congress on
  • Conference_Location
    Santa Clara, CA
  • Print_ISBN
    978-0-7695-5006-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2013.19
  • Filename
    6597121