Integrating process management and event processing in smart factories: A systems architecture and use cases

Seiger, Ronny; Malburg, Lukas; Weber, Barbara; Bergmann, Ralph

doi:10.1016/j.jmsy.2022.05.012

Cited by 29 publications

(38 citation statements)

References 39 publications

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“…In IoT environments where business processes are executed, a large amount of procedural data is generated. The use of these can enable the development of innovative applications in process control [23,28,31], process conformance checking [11,35,36], or process enhancement [29], among others. Here, in particular, process mining techniques could be applied [23,31,32,38].…”

Section: Introductionmentioning

confidence: 99%

“…The use of these can enable the development of innovative applications in process control [23,28,31], process conformance checking [11,35,36], or process enhancement [29], among others. Here, in particular, process mining techniques could be applied [23,31,32,38]. In contrast to many other domains, in the IoT domain, the context in which certain operations are performed is a particularly important factor [7].…”

Section: Introductionmentioning

confidence: 99%

“…One domain where process orchestration/execution [24] and IoT meet is the manufacturing domain [25,29]. Here context is mainly derived from sensors that monitor the execution environment and resources during execution [4,13,31,33]. In domains as manufacturing, process orchestration/execution [23][24][25]29,31] often relies on Internet of Things (IoT) technology.…”

Section: Introductionmentioning

confidence: 99%

“…Here context is mainly derived from sensors that monitor the execution environment and resources during execution [4,13,31,33]. In domains as manufacturing, process orchestration/execution [23][24][25]29,31] often relies on Internet of Things (IoT) technology. While IoT actuators can be used to automate process tasks, IoT sensors and tags can be used to closely monitor the execution environment and involved resources [4,13,31,33].…”

Section: Introductionmentioning

confidence: 99%

“…In domains as manufacturing, process orchestration/execution [23][24][25]29,31] often relies on Internet of Things (IoT) technology. While IoT actuators can be used to automate process tasks, IoT sensors and tags can be used to closely monitor the execution environment and involved resources [4,13,31,33]. IoT technology can therefore capture the context in which certain process tasks are performed, which is a particularly important factor for techniques such as process mining [7,32] to better understand and analyze the manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Grüger¹,

Malburg²,

Mangler³

et al. 2022

Preprint

Self Cite

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

show abstract

GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning

Hoffmann

Malburg

Bach

et al. 2022

Lecture Notes in Computer Science

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Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum

Marrella

Matulevičius²,

Gabryelczyk³

et al. 2022

Lecture Notes in Business Information Processing

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Since IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare can collect data on the execution of the processes, there is a growing awareness of the opportunity to use IoT data for process mining (PM). However, several challenges need to be addressed to enable IoT-enhanced PM, among which the need for a standard IoT-enhanced event log model. In this paper, we identify four main challenges for the integration of IoT data in event logs, from which 10 requirements for an IoT-enhanced event log model are derived. We then analyse the two main current event log models, the extensible event stream (XES) and the object-centric event log (OCEL), and confront them with the requirements, showing that both present important limitations to storing IoT-enhanced event logs. Based on this analysis, we conclude that a comprehensive data model is required to develop more advanced IoT-enhanced PM techniques.

show abstract

Integrating process management and event processing in smart factories: A systems architecture and use cases

Cited by 29 publications

References 39 publications

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

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

GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning

Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum

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