Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1692
Title: | Complexity Clustering of BPMN Models: Initial Experiments with the K-means Algorithm |
Authors: | Fotoglou, Chrysa Tsakalidis, George Vergidis, Kostas Chatzigeorgiou, Alexander |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences FRASCATI::Social sciences::Economics and Business::Business and Management |
Keywords: | Business intelligence Business process complexity Data mining Cluster analysis Multi-criteria decision making BPMN K-means |
Issue Date: | 2020 |
Volume: | 384 |
First Page: | 57 |
Last Page: | 69 |
Volume Title: | Decision Support Systems X: Cognitive Decision Support Systems and Technologies |
Part of Series: | Lecture Notes in Business Information Processing |
Part of Series: | Lecture Notes in Business Information Processing |
Abstract: | This paper introduces a method to assess the complexity of process models by utilizing a cluster analysis technique. The presented method aims to facilitate multi-criteria decision making and process objective management, through the combination of specific quality indicators. This is achieved by leveraging established complexity metrics from literature, and combining three complementary ones (i.e. NOAJS, CFC and CNC) to a single weighted measure, offering an integrated scheme for evaluating complexity. K-means clustering algorithm is implemented on 87 eligible models, out of a repository of 1000 models, and classifies them to corollary clusters that correspond to complexity levels. By assigning weighted impact on specific complexity metrics -an action that leads to the production of threshold values- cluster centroids can fluctuate, thus produce customized model categorizations. The paper demonstrates the application of the proposed method on existing business process models from relevant literature. The assessment of their complexity is performed by comparing the weighted sum of each model to the defined thresholds and proves to be a straightforward and efficient procedure. |
URI: | https://doi.org/10.1007/978-3-030-46224-6_5 https://ruomo.lib.uom.gr/handle/7000/1692 |
ISBN: | 978-3-030-46223-9 978-3-030-46224-6 |
ISSN: | 1865-1348 1865-1356 |
Other Identifiers: | 10.1007/978-3-030-46224-6_5 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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ICDSST2020_CFotoglou.pdf | 326,05 kB | Adobe PDF | View/Open |
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