Comparing and Combining Predictive Business Process Monitoring Techniques

Metzger, Andreas; Leitner, Philipp; Ivanović, Dragan; Schmieders, Eric; Franklin, Rod; Carro, Manuel; Dustdar, Schahram; Pohl, Klaus

doi:10.1109/tsmc.2014.2347265

Cited by 108 publications

(75 citation statements)

References 35 publications

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“…4. Our earlier work indicated reasonably good prediction accuracy (>70%) for this point in process execution, while still leaving time to execute actions required to respond to violations or mitigate their effects [22].…”

Section: Industry Data Set and Experiments Executionmentioning

confidence: 81%

“…We use artificial neural networks (ANNs [15]) as prediction models, which have shown good success in our earlier work [21,22]. In particular, we use multilayer perceptrons as a specific form of ANNs.…”

Section: Methodsmentioning

confidence: 99%

“…In our experiments, we predict, during process execution, whether a transport process instance violates its stipulated delivery deadline. Predictions may be performed at any point in time during process execution, but the point of prediction has an impact on prediction accuracy; e.g., earlier points usually imply lower prediction accuracy [22]. For our experiments, we perform the predictions immediately after the synchronization point of the incoming transport processes as indicated in Fig.…”

Section: Industry Data Set and Experiments Executionmentioning

confidence: 99%

“…Research on predictive process monitoring (such as [3,5,7,12,13,22,33]) and proactive adaptation (such as [1,20,23]) focused on aggregate prediction accuracy. Only recently, reliability estimates have been considered in the context of predictive process monitoring.…”

Section: Related Workmentioning

confidence: 99%

“…Predictive process monitoring predicts how an ongoing process instance will unfold up to its completion [19,22]. If a potential problem is predicted, this problem is analyzed and adaptation decisions are triggered to prevent or mitigate the predicted problem.…”

Section: Introductionmentioning

confidence: 99%

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Risk-Based Proactive Process Adaptation

Metzger

Bohn

2017

Service-Oriented Computing

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Abstract. Proactive process adaptation facilitates preventing or mitigating upcoming problems during process execution, such as process delays. Key for proactive process adaptation is that adaptation decisions are based on accurate predictions of problems. Previous research focused on improving aggregate accuracy, such as precision or recall. However, aggregate accuracy provides little information about the error of an individual prediction. In contrast, so called reliability estimates provide such additional information. Previous work has shown that considering reliability estimates can improve decision making during proactive process adaptation and can lead to cost savings. So far, only constant cost functions have been considered. In practice, however, costs may differ depending on the magnitude of the problem; e.g., a longer process delay may result in higher penalties. To capture different cost functions, we exploit numeric predictions computed from ensembles of regression models. We combine reliability estimates and predicted costs to quantify the risk of a problem, i.e., its probability and its severity. Proactive adaptations are triggered if risks are above a pre-defined threshold. A comparative evaluation indicates that cost savings of up to 31%, with 14.8% savings on average, may be achieved by the risk-based approach.

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Section: Industry Data Set and Experiments Executionmentioning

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Industry Data Set and Experiments Executionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk-Based Proactive Process Adaptation

Metzger

Bohn

2017

Service-Oriented Computing

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Predictive monitoring using machine learning algorithms and a real‐life example on schizophrenia

Huberts

Does

Ravesteijn

et al. 2021

Quality & Reliability Eng

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Predictive process monitoring aims to produce early warnings of unwanted events. We consider the use of the machine learning method extreme gradient boosting as the forecasting model in predictive monitoring. A tuning algorithm is proposed as the signaling method to produce a required false alarm rate. We demonstrate the procedure using a unique data set on mental health in the Netherlands. The goal of this application is to support healthcare workers in identifying the risk of a mental health crisis in people diagnosed with schizophrenia. The procedure we outline offers promising results and a novel approach to predictive monitoring.

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Utilizing Twitter Data for Identifying and Resolving Runtime Business Process Disruptions

Ayoub

Elgammal

2018

Lecture Notes in Computer Science

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Comparing and Combining Predictive Business Process Monitoring Techniques

Cited by 108 publications

References 35 publications

Risk-Based Proactive Process Adaptation

Risk-Based Proactive Process Adaptation

Predictive monitoring using machine learning algorithms and a real‐life example on schizophrenia

Utilizing Twitter Data for Identifying and Resolving Runtime Business Process Disruptions

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