Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Accurately 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.
Recommended Citation
Rodrigues Neubauer, Thais; Peeperkorn, Jari; De Weerdt, Jochen; Fantinato, Marcelo; and Marques Peres, Sarajane, "Enhancing Remaining Time Prediction in Business Processes through Graph Embedding" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/da/data_science/3
Enhancing Remaining Time Prediction in Business Processes through Graph Embedding
Hilton Waikoloa Village, Hawaii
Accurately 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.
https://aisel.aisnet.org/hicss-58/da/data_science/3