Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtime

Stertz, Florian; Rinderle-Ma, Stefanie; Mangler, Juergen

doi:10.1007/978-3-030-58666-9_12

Cited by 25 publications

(28 citation statements)

References 20 publications

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“…To exploit this information in conformance checking, the data from external sensors can be used, which is typically stored as a time sequence. Time sequences can be compared using, for example, dynamic time warping [13,10].…”

Section: Methodsmentioning

confidence: 99%

“…This is expensive and maximally invasive, because the way how information on the execution of process instances for process mining is stored, is in so called process execution logs (logs for short) 1 . On top of that, in order to meet the full spectrum of possible data sources in the manufacturing domain, the collection of process execution data has to be augmented with the collection of sensor and machining data [13].…”

Section: Introductionmentioning

confidence: 99%

“…The shopfloor worker monitors the resource behavior based on sensor data and time. Deviations in the sensor data hint to problems with the workpiece quality and occur due to resource degradation [13]. Time-deviations are mostly caused by humans, e.g., someone stepping into the safety area of a machine causing a delay, and hint to problems with work organization.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, TI-DATE features process logging mechanisms including the linking of sensor and other machining parameters to the logs. Finally, TIDATE features process mining techniques, particularly time and data-aware conformance checking during runtime [13,12]. TIDATE is applied in a manufacturing environment.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain

Stertz,

Mangler,

Rinderle-Ma

2021

Preprint

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Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process mining creates high expectations, but its implementation and usage by manufacturing experts such as process supervisors and shopfloor workers remain unclear to a certain extent. Reason 1 is that even though manufacturing allows for well-structured processes, the actual workflow is rarely captured in a process model. Even if a model is available, a software for orchestrating and logging the execution is often missing. Reason 2 refers to the work reality in manufacturing: a process instance is started by a shopfloor worker who then turns to work on other things. Hence continuous monitoring of the process instances does not happen, i.e., process monitoring is merely a secondary task, and the shopfloor worker can only react to problems/errors that have already occurred. 1 and 2 motivate the goals of this study that is driven by Technical Action Research (TAR). Based on the experimental artifact TIDATE -a lightweight process execution and mining framework-it is studied how the correct execution of process instances can be ensured and how a data set suitable for process mining can be generated at run time in a real-world setting. Secondly, it is investigated whether and how process mining supports domain experts during process monitoring as a secondary task. The findings emphasize the importance of online conformance checking in manufacturing and show how appropriate data sets can be identified and generated.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain

Stertz,

Mangler,

Rinderle-Ma

2021

Preprint

Self Cite

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“…Other studies focus on the cause-effect relationship between data and controlflow perspective. For example, Stertz et al [18] and Adams et al [19] find change points from multiple perspectives and try to determine if changes from one perspective can potentially cause changes from another perspective (eg. if the change of room temperature can cause the change of the process control flow structure).…”

Section: Introductionmentioning

confidence: 99%

Detecting Branching Condition Changes in Process Models

Lu¹,

Chen²,

Poon³

2021

Preprint

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Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

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Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

Ehrendorfer

Mangler

Rinderle-Ma

2021

Lecture Notes in Business Information Processing

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One target of process analysis, monitoring, and prediction is the process outcome, e.g., the quality of a produced part. The process outcome is affected by process execution data, including (external) sensor data streams, e.g., indicating an overheating machine. Challenges are to select the "right" sensors -possibly a multitude of sensors is availableand to specify how the sensor data streams are aggregated and used to calculate the impact on the outcome. This paper introduces process task annotations to specify the selected sensors, their aggregation, and initial impact functions. The initial impact functions are then refined, e.g., threshold values and the impact of sensor data streams are determined. The approach is prototypically implemented. Its applicability is demonstrated based on a real-world manufacturing scenario.

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Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtime

Cited by 25 publications

References 20 publications

The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain

The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain

Detecting Branching Condition Changes in Process Models

Sensor Data Stream Selection and Aggregation for the Ex Post Discovery of Impact Factors on Process Outcomes

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