IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Recovery and Refinement of Business Process Models for Web Applications

Recovery and Refinement of Business Process Models for Web Applications
View Sample PDF
Author(s): Alessandro Marchetto (Independent Researcher, Italy)and Chiara Di Francescomarino (Fondazione Bruno Kessler (CIT), Italy)
Copyright: 2018
Pages: 44
Source title: Application Development and Design: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-3422-8.ch010

Purchase

View Recovery and Refinement of Business Process Models for Web Applications on the publisher's website for pricing and purchasing information.

Abstract

Web Applications (WAs) have been often used to expose business processes to the users. WA modernization and evolution are complex and time-consuming activities that can be supported by software documentation (e.g., process models). When, as often happens, documentation is missing or is incomplete, documentation recovery and mining represent an important opportunity for reconstructing or completing it. Existing process-mining approaches, however, tend to recover models that are quite complex, rich, and intricate, thus difficult to understand and use for analysts and developers. Model refinement approaches have been presented in the literature to reduce the model complexity and intricateness while preserving the capability of representing the relevant information. In this chapter, the authors summarize approaches to mine first and refine later business process models from existing WAs. In particular, they present two process model refinement approaches: (1) re-modularization and (2) reduction. The authors introduce the techniques and show how to apply them to WAs.

Related Content

Babita Srivastava. © 2024. 21 pages.
Sakuntala Rao, Shalini Chandra, Dhrupad Mathur. © 2024. 27 pages.
Satya Sekhar Venkata Gudimetla, Naveen Tirumalaraju. © 2024. 24 pages.
Neeta Baporikar. © 2024. 23 pages.
Shankar Subramanian Subramanian, Amritha Subhayan Krishnan, Arumugam Seetharaman. © 2024. 35 pages.
Charu Banga, Farhan Ujager. © 2024. 24 pages.
Munir Ahmad. © 2024. 27 pages.
Body Bottom