Prediction of Business Process Model Quality Based on Structural Metrics

Sánchez-González, Laura; García, Félix; Mendling, Jan; Ruíz, Francisco; Piattini, Mario

doi:10.1007/978-3-642-16373-9_35

Cited by 29 publications

(41 citation statements)

References 6 publications

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“…[19] CNC Coefficient of Network Complexity calculated as: CNC = NOF / NOAJS. [20] Diameter Length of the longest path from a start node to an end node. [20] Density Ratio of the total number of arcs to the maximum number of arcs.…”

Section: Overview Of Complexity Metrics For Business Process Models Amentioning

confidence: 99%

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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%

“…[20] Diameter Length of the longest path from a start node to an end node. [20] Density Ratio of the total number of arcs to the maximum number of arcs. [20] AGD Average Gateway Degree is the average of the number of both incoming and outgoing arcs of the gateway nodes in the process model.…”

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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“…In this subsection we make a comparative analysis of processes models discovered from the event logs and corresponding BPMN models obtained as a result of conversions using various metrics [34]. We will consider the following metrics: the number of nodes, the diameter (the maximal length of a shortest path from a start node to a node of the graph) and density (ratio of the total number of arcs to the maximum possible number of arcs of the graph).…”

Section: Comparative Analysis Of the Models Discoveredmentioning

confidence: 99%

“…In order to estimate the advantages of using the BPMN notation for mining, we additionally compare the complexity of the models produced by the existing control flow discovery algorithms and the complexity of the corresponding BPMN models. We use the various metrics, such as the number of nodes, density, and diameter [34] for this evaluation. We present not only theoretical but also practical evaluations based on real-life event logs.…”

Section: Introductionmentioning

confidence: 99%

Process mining using BPMN: relating event logs and process models

et al. 2015

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“…But prevalent success measures for individual modeling sessions primarily involve some form of model quality measure [7][8][9][10]. While it is undisputed that the quality of a business process model is relevant to modeling success it is not the only and perhaps not even the most important success factor.…”

Section: Introductionmentioning

confidence: 99%

Business Process Model Similarity as a Proxy for Group Consensus

Rittgen

2011

Lecture Notes in Business Information Processing

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Abstract.Consensus is an important measure for the success of any business process modeling effort. Although intensively studied in the general literature on group processes, consensus has hardly been considered in business process modeling and never seriously measured. We define consensus as the level of agreement of group members' views on the process and introduce business process similarity as a proxy. We validate the measure by comparing it to an existing self-reported measure of consensus. Keywords:Business process modeling, model similarity, group consensus, mental model, view, visualization. IntroductionThe literature on business process modeling is vast and the importance of measuring the success of process modeling projects and sessions has been widely recognized [1][2][3][4][5][6]. But prevalent success measures for individual modeling sessions primarily involve some form of model quality measure [7][8][9][10]. While it is undisputed that the quality of a business process model is relevant to modeling success it is not the only and perhaps not even the most important success factor. The reason for this is twofold: the process model itself is a social construction, and its purpose is again to support some social process, e.g. a change project or system development project. In other words: the model documents the results of one social process (modeling) and serves as a point of departure for another one.If the model were to be processed by a computer its quality would be of prime importance to ensure correct interpretation by the machine. But the results that are documented in the model are primarily the mutual knowledge that has been developed in the modeling session, the conflicts that had to be solved on the way, and the consensus that has been achieved among the group members as a result.It is precisely this consensus that is a prerequisite for people's commitment to the ensuing change project, for example. Often a poor model with high consensus goes further than a good model with little consensus. Hence consensus is a major result that needs to be achieved in business process modeling sessions much like in many other forms of group work.

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Prediction of Business Process Model Quality Based on Structural Metrics

Cited by 29 publications

References 6 publications

Measuring Complexity of Business Process Models Integrated with Rules

Measuring Complexity of Business Process Models Integrated with Rules

Process mining using BPMN: relating event logs and process models

Business Process Model Similarity as a Proxy for Group Consensus

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