An object-centric core metamodel for IoT-enhanced event logs

TL;DR

The paper introduces CORE, a unifying object-centric metamodel for IoT-enhanced event logs to bridge fragmentation across IoT-BPM data formats. Rooted in OCEL 2.0, CORE captures IoT and process events, supports multiple data sources, and accommodates semantic annotations and metadata, enabling cross-domain interoperability. A prototypical Python implementation and an OCEL 2.0 extension demonstrate translation from existing formats (DataStream, NICE, CAIRO) across near real-world use cases (smart spaces, manufacturing, healthcare). The evaluation shows CORE can represent diverse logs with minimal information loss, improving data exchange and collaborative research in process mining for IoT-enabled environments. The work also discusses trade-offs and future work toward official OCEL extension status and integration with PM4PY.

Abstract

Advances in Internet-of-Things (IoT) technologies have prompted the integration of IoT devices with business processes (BPs) in many organizations across various sectors, such as manufacturing, healthcare and smart spaces. The proliferation of IoT devices leads to the generation of large amounts of IoT data providing a window on the physical context of BPs, which facilitates the discovery of new insights about BPs using process mining (PM) techniques. However, to achieve these benefits, IoT data need to be combined with traditional process (event) data, which is challenging due to the very different characteristics of IoT and process data, for instance in terms of granularity levels. Recently, several data models were proposed to integrate IoT data with process data, each focusing on different aspects of data integration based on different assumptions and requirements. This fragmentation hampers data exchange and collaboration in the field of PM, e.g., making it tedious for researchers to share data. In this paper, we present a core model synthesizing the most important features of existing data models. As the core model is based on common requirements, it greatly facilitates data sharing and collaboration in the field. A prototypical Python implementation is used to evaluate the model against various use cases and demonstrate that it satisfies these common requirements.

Figures (14)

• Figure 1: BPMN diagram showing the main steps of the running example process.
• Figure 2: Metamodel of the OCEL 2.0 format (taken from berti2024ocel).
• Figure 3: DataStream XES Metamodel datastream
• Figure 4: Main concepts of the NICE model bertrand2023nice.
• Figure 5: CAIRO metamodel franceschetti2023event