# Recent Advances in Data-Driven Business Process Management

arXiv: https://arxiv.org/abs/2406.01786 · HTML: https://mlsanity.com/paper/2406.01786/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 116 references that are in the map.
- First posted on arXiv 3 Jun 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [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)
- [Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning](https://mlsanity.com/paper/2012.02462/) (2020)
- [Prescriptive Business Process Monitoring for Recommending Next Best Actions](https://mlsanity.com/paper/2008.08693/) (2020)
- [Alarm-Based Prescriptive Process Monitoring](https://mlsanity.com/paper/1803.08706/) (2018)
- [An Approach for Process Model Extraction By Multi-Grained Text Classification](https://mlsanity.com/paper/1906.02127/) (2019)
- [Prescriptive Process Monitoring: Quo Vadis?](https://mlsanity.com/paper/2112.01769/) (2021)
- [PET: An Annotated Dataset for Process Extraction from Natural Language Text](https://mlsanity.com/paper/2203.04860/) (2022)
- [ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence](https://mlsanity.com/paper/2306.01771/) (2023)
- [Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text](https://mlsanity.com/paper/2305.03960/) (2023)
- [Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs](https://mlsanity.com/paper/2104.00362/) (2021)
- [Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Mining](https://mlsanity.com/paper/2010.11289/) (2020)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [Can recurrent neural networks learn process model structure?](https://mlsanity.com/paper/2212.06430/) (2022)
- [A framework to evaluate the viability of robotic process automation for business process activities](https://mlsanity.com/paper/2007.10900/) (2020)
- [On the Composition of the Long Tail of Business Processes: Implications from a Process Mining Study](https://mlsanity.com/paper/2011.13188/) (2020)

## Built on by (most cited first, 2 of 2)

- [Assisted Data Annotation for Business Process Information Extraction from Textual Documents](https://mlsanity.com/paper/2410.01356/) (2024)
- [FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring](https://mlsanity.com/paper/2508.20021/) (2025)

## Abstract (arXiv)

The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM.