Abstract
Process-aware Recommender systems are information systems designed to monitor the execution of processes, predict their outcomes, and suggest effective interventions to achieve better results, with respect to reference KPIs (Key Performance Indicators). Interventions typically consist of suggesting an activity to be assigned to a certain resource. State of the art typically proposes interventions for single cases in isolation. However, since resources are shared among cases, this might impact the effectiveness of the available interventions for other cases that would require one. As result, the overall KPI improvement is partially hampered. This paper proposes an approach to assign resources to needed cases, aiming to improve the overall KPI values for all cases together, namely the summation of KPI values for all cases. Experiments conducted on two real-life case studies illustrate that globally considering all needing cases together allows a better global KPI improvement, compared with a more greedy approach where interventions are proposed one after the other.
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Notes
- 1.
The operator * refers to the Kleene star: given a set A, \(A^*\) contains all the possible finite sequences of elements belonging to A.
- 2.
Given a trace \(\sigma \), \(|\sigma |\) indicates the number of events in \(\sigma \).
- 3.
Code available at https://github.com/Pado123/prescriptive_global_optimization.
- 4.
- 5.
The random percentage p was drawn from a uniform distribution
, repeating the experiment for its stochastic validity.
, repeating the experiment for its stochastic validity.References
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Acknowledgement
The PhD. scholarship of Mr. Padella is partly funded by IBM Italy, and by the BMCS Doctoral Program, University of Padua. This research is also supported by the Department of Mathematics, University of Padua, through the BIRD project “Data-driven Business Process Improvement” (code BIRD215924/21).
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Padella, A., de Leoni, M. (2023). Resource Allocation in Recommender Systems for Global KPI Improvement. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-41623-1_15
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