# From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizations

arXiv: https://arxiv.org/abs/2508.08061 · HTML: https://mlsanity.com/paper/2508.08061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 62 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Unitary Evolution Recurrent Neural Networks](https://mlsanity.com/paper/1511.06464/) (2015)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Text-Aware Predictive Monitoring of Business Processes](https://mlsanity.com/paper/2104.09962/) (2021)
- [Exploiting Event Log Event Attributes in RNN Based Prediction](https://mlsanity.com/paper/1904.06895/) (2019)
- [Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks](https://mlsanity.com/paper/2106.08027/) (2021)
- [A Case for Business Process-Specific Foundation Models](https://mlsanity.com/paper/2210.14739/) (2022)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)

## Built on by (most cited first, 1 of 1)

- [D-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoring](https://mlsanity.com/paper/2608.28236/) (2026)

## Abstract (arXiv)

Event logs reflect the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms these data into value by creating process-related predictions that provide the insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, which prevents some organizations from utilizing PPM. The transfer learning-based PPM technique presented in this paper allows organizations without suitable event data or other relevant resources to implement PPM for effective decision support. This technique is instantiated in both a real-life intra- and an inter-organizational use case, based on which numerical experiments are performed using event logs for IT service management processes. The results of the experiments suggest that knowledge of one business process can be transferred to a similar business process in the same or a different organization to enable effective PPM in the target context. The proposed technique allows organizations to benefit from transfer learning in intra- and inter-organizational settings by transferring resources such as pre-trained models within and across organizational boundaries.