# From Internet of Things Data to Business Processes: Challenges and a Framework

arXiv: https://arxiv.org/abs/2405.08528 · HTML: https://mlsanity.com/paper/2405.08528/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 101 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 4 of 4)

- [Event Abstraction for Process Mining using Supervised Learning Techniques](https://mlsanity.com/paper/1606.07283/) (2016)
- [Temporal Conformance Checking at Runtime based on Time-infused Process Models](https://mlsanity.com/paper/2008.07262/) (2020)
- [SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data](https://mlsanity.com/paper/2206.11392/) (2022)
- [An IoT-Enriched Event Log for Process Mining in Smart Factories](https://mlsanity.com/paper/2209.02702/) (2022)

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

- [LLM-based event abstraction and integration for IoT-sourced logs](https://mlsanity.com/paper/2409.03478/) (2024)

## Abstract (arXiv)

The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. The IoT community has a strong focus on hardware, connectivity and data; the BPM community focuses mainly on finding, controlling, and enhancing the structured interactions among the IoT devices in processes. While the field of Process Mining deals with the extraction of process models and process analytics from process event logs, the data produced by IoT sensors often is at a lower granularity than these process-level events. The fundamental questions about extracting and abstracting process-related data from streams of IoT sensor values are: (1) Which sensor values can be clustered together as part of process events?, (2) Which sensor values signify the start and end of such events?, (3) Which sensor values are related but not essential? This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining. The framework is meant to provide a generic sequence of abstract steps to guide the event extraction, abstraction, and correlation, with variation points for plugging in specific analysis techniques and algorithms for each step. To assess the completeness of the framework, we present a set of challenges, how they can be tackled through the framework, and an example on how to instantiate the framework in a real-world demonstration from the field of smart manufacturing. Based on this framework, future research can be conducted in a structured manner through refining and improving individual steps.