Square Complexity Metrics for Business Process Models

Kluza, Krzysztof; Nalepa, Grzegorz J.; Lisiecki, Janusz

doi:10.1007/978-3-319-03677-9_6

Cited by 24 publications

(20 citation statements)

References 31 publications

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“…Wider overview of the current state of the art in various areas of business process metrics can be found in papers [26,27,28,29,30,31] as well as in the book [21]. Table 2 presents the selected complexity metrics for rule bases.…”

Section: Overview Of Complexity Metrics For Business Process Models Amentioning

confidence: 99%

Measuring Complexity of Business Process Models Integrated with Rules

Kluza

2015

Artificial Intelligence and Soft Computing

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Abstract. Complexity assessment is often used in software and knowledge engineering for controlling the quality and improving models. In fact, complexity is one of the main factors affecting the understability and maintainability of models. Although there are many complexity measures that can be used in order to assess the complexity of process models or rule bases, the complexity of the integrated model of processes with rules is not addressed and constitutes a challenging issue. In this paper, we consider a new metric that is suitable for measuring the integrated models and present a short evaluation based on the selected cases of different size.

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Section: Overview Of Complexity Metrics For Business Process Models Amentioning

confidence: 99%

Measuring Complexity of Business Process Models Integrated with Rules

Kluza

2015

Artificial Intelligence and Soft Computing

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“…(3) Petri net entity count (places and transitions) and ( 4) edge count are used as structural simplicity measures, ensuring that conformance quality has not been achieved by sacrificing model simplicity and comprehensibility. Entity and arc counts have existing uses in process model evaluation [14,17], and were preferred here over behavioural simplicity measures [16], though these measures also have limitations, including specificity to Petri nets, and insensitivity to the stochastic perspective of GSPNs. The duration of a discovery process was also captured, and direct discovery times are compared with combined runtimes for discovery and estimation.…”

Section: Evaluation Designmentioning

confidence: 99%

Stochastic Process Discovery by Weight Estimation

Burke

Leemans

Wynn

2021

Lecture Notes in Business Information Processing

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Many algorithms now exist for discovering process models from event logs. These models usually describe a control flow and are intended for use by people in analysing and improving real-world organizational processes. The relative likelihood of choices made while following a process (i.e., its stochastic behaviour) is highly relevant information which few existing algorithms make available in their automatically discovered models. This can be addressed by automatically discovered stochastic process models.We introduce a framework for automatic discovery of stochastic process models, given a control-flow model and an event log. The framework introduces an estimator which takes a Petri net model and an event log as input, and outputs a Generalized Stochastic Petri net. We apply the framework, adding six new weight estimators, and a method for their evaluation. The algorithms have been implemented in the opensource process mining framework ProM. Using stochastic conformance measures, the resulting models have comparable conformance to existing approaches and are shown to be calculated more efficiently.

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“…A plethora of works on software complexity metrics exists [18][19][20]. Additionally, software metrics have been transformed and applied on processes and workflow nets in a vast array of studies [21][22][23][24][25][26][27]. Most of these studies focus on the BPMN standard.…”

Section: Related Workmentioning

confidence: 99%

“…This Density metric was suggested by [25], and later discussed by [28] and [21]. It aims at measuring the ratio of the actual and the maximum number of arcs in the BPMN model.…”

Section: Density (Dens)mentioning

confidence: 99%

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Complexity metrics for DMN decision models

Hasić

Vanthienen

2019

Computer Standards & Interfaces

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Complexity impairs the maintainability and understandability of conceptual models. Complexity metrics have been used in software engineering and business process management (BPM) to capture the degree of complexity of conceptual models. The recent introduction of the Decision Model and Notation (DMN) standard provides opportunities to shift towards the Separation of Concerns paradigm when it comes to modelling processes and decisions.However, unlike for processes, no studies exist that address the representational complexity of DMN decision models. In this paper, we provide an initial set of complexity metrics for DMN models. We gather insights from the process modelling and software engineering

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Square Complexity Metrics for Business Process Models

Cited by 24 publications

References 31 publications

Measuring Complexity of Business Process Models Integrated with Rules

Measuring Complexity of Business Process Models Integrated with Rules

Stochastic Process Discovery by Weight Estimation

Complexity metrics for DMN decision models

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