Scalable and trustworthy cross-enterprise WfMSs by cloud collaboration

Gwan Hwan Hwang, Yi Chan Kao, Yu Cheng Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Congress on Big Data, BigData 2013
Pages70-77
Number of pages8
DOIs
Publication statusPublished - 2013 Oct 28
Event2013 IEEE International Congress on Big Data, BigData 2013 - Santa Clara, CA, United States
Duration: 2013 Jun 272013 Jul 2

Publication series

NameProceedings - 2013 IEEE International Congress on Big Data, BigData 2013

Other

Other2013 IEEE International Congress on Big Data, BigData 2013
CountryUnited States
CitySanta Clara, CA
Period13/6/2713/7/2

Keywords

  • Cloud
  • Multitenancy
  • WfMS

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Hwang, G. H., Kao, Y. C., & Hsiao, Y. C. (2013). Scalable and trustworthy cross-enterprise WfMSs by cloud collaboration. In Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013 (pp. 70-77). [6597121] (Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013). https://doi.org/10.1109/BigData.Congress.2013.19