Source of Publication
2021 17th International Conference on Web Information Systems and Technologies (WEBIST)
Process mining is the art and science of (semi)automatically generating business processes from a large number of logs coming from potentially heterogeneous systems. With the recent advent of Industry 4.0 analog enterprise environments such as floor shops and long supply chains are bound to full digitization. In this context interest in process mining has been invigorated. Multilayer graphs constitute a broad class of combinatorial objects for representing, among others, business processes in a natural and intuitive way. Specifically the concepts of state and transition, central to the majority of existing approaches, are inherent in these graphs and coupled with both semantics and graph signal processing. In this work a model for representing business processes with multilayer graphs along with related analytics based on information theory are proposed. As a proof of concept, the latter have been applied to large synthetic datasets of increasing complexity and with real world proper ties, as determined by the recent process mining scientific literature, with encouraging results.
Process Mining, Industry 4.0, Graph Signal Processing, Graph Mining, Multilayer Graphs, PM4Py, Neo4j
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Drakopoulos, Georgios; Kafeza, Eleanna; Mylonas, Phivos; and Sioutas, Spyros, "Process Mining Analytics for Industry 4.0 with Graph Signal Processing" (2021). All Works. 4693.
Indexed in Scopus
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series