Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs (Extended Version)
Shahrzad Khayatbashi, Olaf Hartig, Amin Jalali
TL;DR
The paper addresses the gap in capturing temporal attribute changes within object-centric event data by introducing Temporal Event Knowledge Graphs (tEKG), an extension of Event Knowledge Graphs (EKG). It formalizes tEKG as an LPG conforming to an extended schema with snapshots that record time-specific object states, while preserving backward compatibility with EKG. A transformation algorithm from OCEL 2.0 logs to tEKG is defined, with auxiliary procedures for node/edge creation and directly-follows edge processing, and implemented with publicly available source code. This approach enables accurate temporal analysis across multiple interacting objects, leveraging existing EKG/OCEPM tooling while capturing dynamic attribute histories. The work lays groundwork for formal definitions via case studies and practical applications in business process analysis and data-driven decision making.
Abstract
Event logs play a fundamental role in enabling data-driven business process analysis. Traditionally, these logs track events related to a single object, known as the case, limiting the scope of analysis. Recent advancements, such as Object-Centric Event Logs (OCEL) and Event Knowledge Graphs (EKG), capture better how events relate to multiple objects. However, attributes of objects can change over time, which was not initially considered in OCEL or EKG. While OCEL 2.0 has addressed some of these limitations, there remains a research gap concerning how attribute changes should be accommodated in EKG and how OCEL 2.0 logs can be transformed into EKG. This paper fills this gap by introducing Temporal Event Knowledge Graphs (tEKG) and defining an algorithm to convert an OCEL 2.0 log to a tEKG.