FBK CIT

Trento, Italy
Trento, Italy
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Tomasi A.,FBK CIT | Marchetto A.,FBK CIT | Di Francescomarino C.,FBK CIT
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Process models play a key role in taking decisions when existing procedures and systems need to be changed and improved. However, these models are often not available or not aligned with the actual process implementation. In these cases, process model recovery techniques can be applied to analyze the existing system implementation and capture the underlying business process models. Several techniques have been proposed in the literature to recover business processes, although the resulting processes are often complex, intricate and thus difficult to understand for business analysts. In this paper, we propose a process reduction technique based on multi-objective optimization, which minimizes at the same time process complexity, non-conformances, and loss of business content. This allows us to improve the process model understandability by decreasing its structural complexity, while preserving the completeness of the described business and domain-specific information. We conducted a case study based on a real-life e-commerce system. Results indicate that by balancing complexity, conformance and business content our technique produces understandable and meaningful reduced process models. © 2012 Springer-Verlag.


Marchetto A.,FBK CIT | Di Francescomarino C.,FBK CIT | Tonella P.,FBK CIT
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

While models are recognized to be crucial for business process management, often no model is available at all or available models are not aligned with the actual process implementation. In these contexts, an appealing possibility is recovering the process model from the existing system. Several process recovery techniques have been proposed in the literature. However, the recovered processes are often complex, intricate and thus difficult to understand for business analysts. In this paper, we propose a process reduction technique based on multi-objective optimization, which at the same time minimizes the process complexity and its non-conformances. This allows us to improve the process model understandability, while preserving its completeness with respect to the core business properties of the domain. We conducted a case study based on a real-life e-commerce system. Results indicate that by balancing complexity and conformance our technique produces understandable and meaningful reduced process models. © 2011 Springer-Verlag.

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