Action-oriented process mining: bridging the gap between insights and actions

Park, Gyunam; Aalst, Wil M. P. van der

doi:10.1007/s13748-022-00281-7

Cited by 15 publications

(8 citation statements)

References 53 publications

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“…Unlike the methods mentioned in the previous paragraph, this is usually obtained through dedicated machine learning models-or modifications thereof-rather than designing specific event-or trace-level abstractions. For instance, many scholars have attempted to make predictions more transparent through the use of explainable machine learning techniques (Verenich, 2019;Stierle et al, 2021;Sindhgatta et al, 2020;Galanti et al, 2020;Park et al, 2022). More related to our present work, (Pauwels & Calders, 2021) propose a technique to avoid the time expenditure caused by the retrain of machine learning models; this is necessary when they are not representative anymore-for instance, when changes occur in the underlying process (caused e.g.…”

Section: Time Optimization and Instance Samplingmentioning

confidence: 88%

Performance-preserving event log sampling for predictive monitoring

et al. 2023

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Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.

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Section: Time Optimization and Instance Samplingmentioning

confidence: 88%

Performance-preserving event log sampling for predictive monitoring

et al. 2023

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“…Finally, the PCM system supports the user in understanding the results and acting on them. To that end, the user support subcomponent recommends actions and countermeasures for non-compliant predicted future events [229],…”

Section: Widening the Scope: Event Prediction Methods In The Pcm Systemmentioning

confidence: 99%

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin¹,

Rinderle-Ma²

2023

Preprint

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Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user's desired future state. If the predicted future events do not comply with this state, actions are taken towards achieving desirable future states. Evidently, event prediction is valuable in many application domains such as business and natural disasters.The diversity of application domains results in a diverse range of methods that are scattered across various research areas which, in turn, use different terminology for event prediction methods. Consequently, sharing methods and knowledge for developing future event prediction methods is restricted. To facilitate knowledge sharing on account of a comprehensive classification, integration, and assessment of event prediction methods, we combine taxonomies and take a systems perspective to integrate event prediction methods into a single system, elicit requirements and assess existing work with respect to the requirements. Based on the assessment, we identify open challenges and discuss future research directions.

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“…First, although process mining may be able to identify some deviations in the process and the use of resources, it does not analyze the environment, strategy, customers, products, and services of the work system that can cause workarounds and be impacted by workarounds. Recent innovations in process mining ( Park & van der Aalst, 2022 ) suggest the potential use of social media data as an additional source for process mining that can partly fill this gap, but these innovations are still in an initial stage of development and were not used in this study.…”

Section: Limitations and Their Implicationsmentioning

confidence: 99%