Modeling, Executing and Monitoring IoT-Aware Processes with BPM Technology

Gallik, Florian; Kirikkayis, Yusuf; Reichert, Manfred

doi:10.1109/icss55994.2022.00023

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…The focus of this work is on investigating the correlation between these sensor events (IoT data) and the execution of process activity executions, and vice versa. Linking IoT data to process executions in CPS promises many benefits [1] including, e.g., the improved monitoring of process activity executions by providing additional context data [11,12], or the real-time decision making in processes based on sensor data [13]. In this work, we distinguish between the linking of IoT data with processes (Process-Awareness of IoT Data) and the linking of process data with IoT (IoT-Awareness of Process Data) [14].…”

Section: Bpm Meets Cps and Iotmentioning

confidence: 99%

An Interactive Method for Detection of Process Activity Executions from IoT Data

2023

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The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages.

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Section: Bpm Meets Cps and Iotmentioning

confidence: 99%

An Interactive Method for Detection of Process Activity Executions from IoT Data

2023

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“…To address this limitation, [14], [7] and [6] rely on IoT data to infer when activities are running. [3] focuses on monitoring multi-party business processes.…”

Section: Related Workmentioning

confidence: 99%

Artifact-Driven Process Monitoring at Scale

Meroni,

Garda

2023

Lecture Notes in Computer Science

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“…Based on the problem statement (cf. Section 1.1), the hands-on experiences we have had with implementing IoT-driven BPs [4,9,11,24,25], and a literature study (cf. Section 4), we refine the research questions RQ1-RQ3 and derive the following objectives (O) from them:…”

Section: Objectivesmentioning

confidence: 99%

“…Visualize IoT involvement in BPs (RQ1): When modeling IoT-driven BPs, IoT involvement should be made transparent and visualized to avoid ambiguity (e.g., to distinguish IoT-related task vs. non IoT-related task), to enable better readability and understandability of the BP (cf. Figure 1), and to represent IoT-specific aspects (e.g., IoT-driven conditions at XOR splits) [4,6,12,24,26].…”

Section: O1mentioning

confidence: 99%

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BPMNE4IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes

et al. 2023

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The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we üresent BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes.

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Modeling, Executing and Monitoring IoT-Aware Processes with BPM Technology

Cited by 11 publications

References 17 publications

An Interactive Method for Detection of Process Activity Executions from IoT Data

An Interactive Method for Detection of Process Activity Executions from IoT Data

Artifact-Driven Process Monitoring at Scale

BPMNE4IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes

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