ProGAN: Toward a Framework for Process Monitoring and Flexibility by Change via Generative Adversarial Networks

Hoffmann, Maximilian; Malburg, Lukas; Bergmann, Ralph

doi:10.1007/978-3-030-94343-1_4

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Thus, they enable the transfer to real-world settings more easily. In our research [16,18,[21][22][23]31], we use a physical factory simulation model from Fischertechnik. The custom model 7 simulates two independently working production lines consisting of two shop floors that are linked for the exchange of workpieces.…”

Section: Physical Factory Simulation Lab Utilizing Bpm Technology At ...mentioning

confidence: 99%

SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data

Grüger¹,

Malburg²,

Mangler³

et al. 2022

Preprint

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Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i. e., via the Internet of Things (IoT). While process execution is about actuation, i. e., actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e. g., produced parts), outcome (e. g., quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision

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Section: Physical Factory Simulation Lab Utilizing Bpm Technology At ...mentioning

confidence: 99%

SensorStream: An XES Extension for Enriching Event Logs with IoT-Sensor Data

Grüger¹,

Malburg²,

Mangler³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Applying MAPE-K control loops for adaptive workflow management in smart factories

2023

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Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM). This is especially the case in cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops are widely used for self-management in these environments and describe four phases for approaching this challenge: Monitor, Analyze, Plan, and Execute. In this paper, we present an architectural solution as well as implementation proposals for using MAPE-K control loops for adaptive workflow management in smart factories. We use Complex Event Processing (CEP) techniques and the process execution states of a Workflow Management System (WfMS) in the monitoring phase. In addition, we apply automated planning techniques to resolve detected exceptional situations and to continue process execution. The experimental evaluation with a physical smart factory shows the potential of the developed approach that is able to detect failures by using IoT sensor data and to resolve them autonomously in near real time with considerable results.

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