JOURNAL ARTICLE

Multi‐perspective business process discovery from messaging systems: State‐of‐the art.

  • Published In: Concurrency & Computation: Practice & Experience, 2023, v. 35, n. 11. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Elleuch, Marwa; Laga, Nassim; Alaoui Ismaili, Oumaima; Gaaloul, Walid 3 of 3

Abstract

Summary: At the stage of identifying and modeling business processes (BP), traditional methods for collecting business expertise are time consuming since they mainly rely on physical communication. Moreover, there are some BP with no formal documentation and their execution relies on implicit knowledge of workers. Therefore, analyzing the logs data generated by messaging systems allowing workers to share such implicit knowledge has been a research area of growing interest. BP knowledge introduced by workers in emails could actually cover multiple BP perspectives that would vary from activity, actors, and data perspectives. This article gives an overview of the current state‐of‐the‐art in multi‐perspective BP discovery from messaging systems as well as the main existing research questions. Different approaches are presented, discussed, and compared against each other on a qualitative level deduced from the defined set of research questions. The resulting overview covers both advantages and disadvantages of current techniques and the research questions that have not been addressed to date. This should provide a solid basis for further research. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Concurrency & Computation: Practice & Experience. 2023/05, Vol. 35, Issue 11, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:15320626
  • DOI:10.1002/cpe.6642
  • Accession Number:163160589
  • Copyright Statement:Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.