Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital

Spoel, Sjoerd van der; Keulen, Maurice van; Amrit, Chintan

doi:10.1007/978-3-642-40919-6_4

Cited by 32 publications

(20 citation statements)

References 22 publications

(27 reference statements)

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“…In the next section we present the primary studies in detail, and classify them using a taxonomy. [25] Lakshmanan et al [21] Conforti et al [5,6] di Francescomarino et al [10] Leontjeva et al [22] van der Spoel et al [43] Verenich et al [45] Castellanos et al [4] Schwegmann et al [36,37], Gha as et al [18]…”

Section: Primary and Subsumed Studiesmentioning

confidence: 99%

Outcome-Oriented Predictive Process Monitoring

Teinemaa

Dumas

Rosa

et al. 2019

ACM Trans. Knowl. Discov. Data

247

281

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Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of ne-grained event data about business process executions, the problem of predictive process monitoring has received substantial a ention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcomeoriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes -e.g., Will the customer complain or not? Will an order be delivered, canceled or withdrawn? Unfortunately, di erent authors have used di erent datasets, experimental se ings, evaluation measures and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of di erent methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.

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Section: Primary and Subsumed Studiesmentioning

confidence: 99%

Outcome-Oriented Predictive Process Monitoring

Teinemaa

Dumas

Rosa

et al. 2019

ACM Trans. Knowl. Discov. Data

247

281

View full text Add to dashboard Cite

show abstract

“…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%

“…Predictive business process monitoring refers to a family of online process monitoring techniques that seek to predict the future state or properties of ongoing executions of a process based on models extracted from historical process executions, stored in so-called event logs. A wide range of predictive business process monitoring methods have been proposed to predict for example compliance violations [24,25], the next activity or the remaining sequence of activities of a process instance [16,53], or quantitative process performance indicators such as the remaining cycle time of a process instance [37,38,52]. These predictions can be used to alert process workers to problematic process instances or to support resource allocation decisions, e.g.…”

Section: Introductionmentioning

confidence: 99%

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.

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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“…The predictive business process monitoring is a research field that is concerned with sequence predictions within the application domain of business process management. The field focuses on several prediction tasks for ongoing instances of a business process, including prediction of an outcome of the process instance [30,75], prediction of the remaining time of a process instance [32,66,72], predicting deadline violations [61], and, most relevant to this work, prediction of the coming business activities with a running instance of a business process [69,72].…”

Section: Predictive Business Process Monitoringmentioning

confidence: 99%

An interdisciplinary comparison of sequence modeling methods for next-element prediction

2020

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Data of sequential nature arise in many application domains in forms of, e.g. textual data, DNA sequences, and software execution traces. Different research disciplines have developed methods to learn sequence models from such datasets: (i) in the machine learning field methods such as (hidden) Markov models and recurrent neural networks have been developed and successfully applied to a wide-range of tasks, (ii) in process mining process discovery techniques aim to generate human-interpretable descriptive models, and (iii) in the grammar inference field the focus is on finding descriptive models in the form of formal grammars. Despite their different focuses, these fields share a common goal -learning a model that accurately describes the behavior in the underlying data. Those sequence models are generative, i.e, they can predict what elements are likely to occur after a given unfinished sequence. So far, these fields have developed mainly in isolation from each other and no comparison exists. This paper presents an interdisciplinary experimental evaluation that compares sequence modeling techniques on the task of nextelement prediction on four real-life sequence datasets. The results indicate that machine learning techniques that generally have no aim at interpretability in terms of accuracy outperform techniques from the process mining and grammar inference fields that aim to yield interpretable models.

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Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital

Cited by 32 publications

References 22 publications

Outcome-Oriented Predictive Process Monitoring

Outcome-Oriented Predictive Process Monitoring

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

An interdisciplinary comparison of sequence modeling methods for next-element prediction

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