Enhancing Remaining Time Prediction in Business Processes through Graph Embedding

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dc.contributor.authorRodrigues Neubauer, Thais
dc.contributor.authorPeeperkorn, Jari
dc.contributor.authorDe Weerdt, Jochen
dc.contributor.authorFantinato, Marcelo
dc.contributor.authorMarques Peres, Sarajane
dc.date.accessioned2024-12-26T21:05:22Z
dc.date.available2024-12-26T21:05:22Z
dc.date.issued2025-01-07
dc.description.abstractAccurately predicting the remaining time of business processes is essential for operational efficiency but remains challenging due to the complex interdependencies among process activities. Traditional approaches often fail to capture these complexities effectively. This paper introduces an approach to improving remaining time prediction through the application of graph embedding to enrich the representation of process activities. The proposed approach enriches the data representation for model training that is agnostic to the prediction algorithm. We detail the graph design and explore embedding parameters, applying them to real-world event logs. Our experimental study demonstrates that our approach can reduce percentual prediction error rates by up to 35% compared to traditional methods, showing the effectiveness of graph embeddings in improving predictive accuracy in complex business environments.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.139
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other61e7608c-79f1-4fe1-90d4-423a7cce212b
dc.identifier.urihttps://hdl.handle.net/10125/108978
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Science and Machine Learning to Support Business Decisions
dc.subjectbusiness process management, graph embedding, predictive process monitoring, process mining, remaining time prediction
dc.titleEnhancing Remaining Time Prediction in Business Processes through Graph Embedding
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage1164

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