Publication
Dynamic definition of binding contracts for high demand business processes
| Summary: | Service Level Agreements (SLAs) often rely on static thresholds derived from historical averages, yet such fixed targets fail to reflect the variability of modern business processes. This work investigates how Process Mining and Machine Learning can support dynamic, context-aware SLA definition. Using real event-log data from a financial institution, the study reconstructs process behaviour, extracts prefix-based features, and develops models to estimate the total duration of a case as it unfolds. Two predictive approaches are examined. The first is a class-based conditional estimator that predicts the final outcome class using Random Forest and XGBoost and assigns a duration based on class-specific historical statistics. All percentiles from 1 to 100 are evaluated to determine the most accurate estimator for each prefix. The second approach trains LSTM networks separately for prefixes 1 through 5, learning temporal patterns from the first k events and using a log transformation to stabilise the skewed duration distribution. Results show that the class-based estimator consistently outperforms the static global-mean SLA baseline, reducing error by nearly 50% at the earliest prefixes. Optimal percentiles vary across prefixes, highlighting the limitations of fixed SLA thresholds. While the LSTM performs poorly at very short prefixes, it improves at later ones and demonstrates the potential of deep learning for duration prediction. Overall, the findings support the shift from static to predictive, data-driven SLA targets, enabling more accurate, adaptive, and operationally meaningful performance expectations. |
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| Main Authors: | Rodrigo, Gustavo Azevedo |
| Subject: | Process Mining Machine Learning Dynamic SLA Predictive Process Monitoring |
| Year: | 2026 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | embargoed access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | Service Level Agreements (SLAs) often rely on static thresholds derived from historical averages, yet such fixed targets fail to reflect the variability of modern business processes. This work investigates how Process Mining and Machine Learning can support dynamic, context-aware SLA definition. Using real event-log data from a financial institution, the study reconstructs process behaviour, extracts prefix-based features, and develops models to estimate the total duration of a case as it unfolds. Two predictive approaches are examined. The first is a class-based conditional estimator that predicts the final outcome class using Random Forest and XGBoost and assigns a duration based on class-specific historical statistics. All percentiles from 1 to 100 are evaluated to determine the most accurate estimator for each prefix. The second approach trains LSTM networks separately for prefixes 1 through 5, learning temporal patterns from the first k events and using a log transformation to stabilise the skewed duration distribution. Results show that the class-based estimator consistently outperforms the static global-mean SLA baseline, reducing error by nearly 50% at the earliest prefixes. Optimal percentiles vary across prefixes, highlighting the limitations of fixed SLA thresholds. While the LSTM performs poorly at very short prefixes, it improves at later ones and demonstrates the potential of deep learning for duration prediction. Overall, the findings support the shift from static to predictive, data-driven SLA targets, enabling more accurate, adaptive, and operationally meaningful performance expectations. |
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