# Extending predictive process monitoring for collaborative processes

arXiv: https://arxiv.org/abs/2409.09212 · HTML: https://mlsanity.com/paper/2409.09212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [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)
- [Time and Activity Sequence Prediction of Business Process Instances](https://mlsanity.com/paper/1602.07566/) (2016)
- [LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/1711.03822/) (2017)
- [Object-centric Process Predictive Analytics](https://mlsanity.com/paper/2203.02801/) (2022)
- [Secure Multi-Party Computation for Inter-Organizational Process Mining](https://mlsanity.com/paper/1912.01855/) (2019)
- [HOEG: A New Approach for Object-Centric Predictive Process Monitoring](https://mlsanity.com/paper/2404.05316/) (2024)
- [Nirdizati: an Advanced Predictive Process Monitoring Toolkit](https://mlsanity.com/paper/2210.09688/) (2022)

## Abstract (arXiv)

Process mining on business process execution data has focused primarily on orchestration-type processes performed in a single organization (intra-organizational). Collaborative (inter-organizational) processes, unlike those of orchestration type, expand several organizations (for example, in e-Government), adding complexity and various challenges both for their implementation and for their discovery, prediction, and analysis of their execution. Predictive process monitoring is based on exploiting execution data from past instances to predict the execution of current cases. It is possible to make predictions on the next activity and remaining time, among others, to anticipate possible deviations, violations, and delays in the processes to take preventive measures (e.g., re-allocation of resources). In this work, we propose an extension for collaborative processes of traditional process prediction, considering particularities of this type of process, which add information of interest in this context, for example, the next activity of which participant or the following message to be exchanged between two participants.