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This manuscript introduces a multimedia business process dataset provided by a German research institute. The dataset was systematically collected in a laboratory environment that reflects the workspace of IT staff managing IT Asset Management (ITAM) processes. It encompasses data from 121 process instances across six basic processes, captured using 37 video recordings from two camera perspectives, motion tracking, environmental sensors, an ITAM system trace, and event log data from user interactions. The data is made available in its raw state and processed form. The object-centric event log format (OCEL) provides discrete business process events from system activities. Event data from reality is supplied as raw video files and logs from environmental sensors. The video files were also manually labelled with identifiable business process activities and their associated entities. This multimedia dataset has been designed as a resource for developing, training, and evaluating process mining techniques based on unstructured data. Consequently, the dataset design emphasizes the traceability of activities and entities across the multimedia data sources.

This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data's heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. Our proposed method effectively produces OCELs when applied to the MIMIC-IV dataset, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach's effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field. [Display omitted] [ABSTRACT FROM AUTHOR]

This manuscript introduces a multimedia business process dataset provided by a German research institute. The dataset was systematically collected in a laboratory environment that reflects the workspace of IT staff managing IT Asset Management (ITAM) processes. It encompasses data from 121 process instances across six basic processes, captured using 37 video recordings from two camera perspectives, motion tracking, environmental sensors, an ITAM system trace, and event log data from user interactions. The data is made available in its raw state and processed form. The object-centric event log format (OCEL) provides discrete business process events from system activities. Event data from reality is supplied as raw video files and logs from environmental sensors. The video files were also manually labelled with identifiable business process activities and their associated entities. This multimedia dataset has been designed as a resource for developing, training, and evaluating process mining techniques based on unstructured data. Consequently, the dataset design emphasizes the traceability of activities and entities across the multimedia data sources., (© 2024 The Author(s).)