# Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Mining

arXiv: https://arxiv.org/abs/2010.11289 · HTML: https://mlsanity.com/paper/2010.11289/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications](https://mlsanity.com/paper/2406.12078/) (2024)
- [Process Mining for Unstructured Data: Challenges and Research Directions](https://mlsanity.com/paper/2401.13677/) (2023)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams](https://mlsanity.com/paper/2604.22476/) (2026)

## Abstract (arXiv)

Process mining is one of the most active research streams in business process management. In recent years, numerous methods have been proposed for analyzing structured process data. Yet, in many cases, it is only the digitized parts of processes that are directly captured from process-aware information systems, and manual activities often result in blind spots. While the use of video cameras to observe these activities could help to fill this gap, a standardized approach to extracting event logs from unstructured video data remains lacking. Here, we propose a reference architecture to bridge the gap between computer vision and process mining. Various evaluation activities (i.e., competing artifact analysis, prototyping, and real-world application) ensured that the proposed reference architecture allows flexible, use-case-driven, and context-specific instantiations. Our results also show that an exemplary software prototype instantiation of the proposed reference architecture is capable of automatically extracting most of the process-relevant events from unstructured video data.