Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions

Senderovich, Arik; Francescomarino, Chiara Di; Ghidini, Chiara; Jorbina, Kerwin; Maggi, Fabrizio Maria

doi:10.1007/978-3-319-65000-5_18

Cited by 62 publications

(52 citation statements)

References 16 publications

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“…As such, various regression methods can be utilized, such as regression trees [11,12] or ensemble of trees, i.e. random forest [53] and XGBoost [39].…”

Section: Family Of Algorithmsmentioning

confidence: 99%

“…As such, a single predictor is fit for all prefixes in the prefix log. This approach has been used in [12], [39] and [45]. • Prefix length bucketing.…”

Section: Prefix Bucketingmentioning

confidence: 99%

“…Although quite simple, decision trees have an advantage in terms of computation performance and interpretability of the results. Other choices include random forest [39,55], support vector regression [36] and extreme gradient boosting [39]. A recent cross-benchmark of 13 state-of-the-art commonly used machine learning algorithms on a set of 165 publicly available classification problems [31] found that gradient tree boosting generally achieves better accuracy than random forest, while random forest is more accurate than decision trees.…”

Section: Predictive Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

Verenich

Dumas

Rosa

et al. 2019

ACM Trans. Intell. Syst. Technol.

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131

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Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.

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“…As such, various regression methods can be utilized, such as regression trees [11,12] or ensemble of trees, i.e. random forest [53] and XGBoost [39].…”

Section: Family Of Algorithmsmentioning

confidence: 99%

“…As such, a single predictor is fit for all prefixes in the prefix log. This approach has been used in [12], [39] and [45]. • Prefix length bucketing.…”

Section: Prefix Bucketingmentioning

confidence: 99%

Section: Predictive Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

Verenich

Dumas

Rosa

et al. 2019

ACM Trans. Intell. Syst. Technol.

Self Cite

131

View full text Add to dashboard Cite

show abstract

“…The work by [54,55] proposes a technique for predicting the remaining processing time using stochastic petri nets. The works by [58,59,42,49] focus on predicting delays in process execution. In [58,59], the authors use queueing theory to address the problem of delay prediction, while [42] explores the delay prediction in the domain of transport and logistics process.…”

Section: Related Workmentioning

confidence: 99%

“…The works by [58,59,42,49] focus on predicting delays in process execution. In [58,59], the authors use queueing theory to address the problem of delay prediction, while [42] explores the delay prediction in the domain of transport and logistics process. In [28], the authors present an ad-hoc predictive clustering approach for predicting process performance.…”

Section: Related Workmentioning

confidence: 99%

Specification-driven predictive business process monitoring

Santoso

Felderer

2019

Softw Syst Model

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Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.

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Predicting process performance: A white‐box approach based on process models

Verenich

Dumas

Rosa

et al. 2019

J Software Evolu Process

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Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real-life event logs and compared against several baselines. KEYWORDS explainable artificial intelligence, flow analysis, predictive process monitoring, process mining, transparent models INTRODUCTIONPredictive business process monitoring techniques seek to predict the future state or properties of ongoing executions of a process based on models extracted from historical event logs. A wide range of predictive business process monitoring techniques have been proposed to predict, for example, compliance violations, 1,2 the next activity or the remaining sequence of activities of a process instance, 3,4 or quantitative process performance indicators, such the remaining cycle time of a process instance. 5-7 These predictions can be used to alert process workers to problematic process instances or to support resource allocation decisions, eg, to allocate additional resources to instances that are at risk of a deadline violation.This article addresses the problem of predicting quantitative process performance indicators, with a specific focus on predicting the remaining cycle time of ongoing process instances. Existing approaches to this problem adopt a "black-box" approach by building stochastic models or regression models which, given a process instance, predict the remaining execution time as a single scalar value, without seeking to explain this prediction in terms of more elementary components. Yet, quantitative performance indicators such as cost or time are aggregations of corresponding performance indicators of the activities composing the process. For example, the cycle time of a process instance with sequentially performed activities consists of the sum of the cycle time of the activities performed in that process instance. In this respect, existing known as flow analysis. 8 The idea of flow analysis is to estimate a quantitative performance indicator at the level of a process by aggregating the estimated values of this perfo...

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Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions

Cited by 62 publications

References 16 publications

Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

Specification-driven predictive business process monitoring

Predicting process performance: A white‐box approach based on process models

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