An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

Weinzierl, Sven; Zilker, Sandra; Brunk, Jens; Revoredo, Kate; Nguyen, An; Matzner, Martin; Becker, Jörg; Eskofier, Bjoern M.

doi:10.48550/arxiv.2005.01194

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…Bohmer et al [3] proposed combining local and global techniques using sequential prediction rules for the next event prediction. For further relevant work on PBPM, an interested reader may refer to [28] and [39] survey studies.…”

Section: Predictive Process Monitoringmentioning

confidence: 99%

ProcessTransformer: Predictive Business Process Monitoring with Transformer Network

Bukhsh,

Saeed,

Dijkman

2021

Preprint

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Predictive business process monitoring focuses on predicting future characteristics of a running process using event logs. The foresight into process execution promises great potentials for efficient operations, better resource management, and effective customer services. Deep learning-based approaches have been widely adopted in process mining to address the limitations of classical algorithms for solving multiple problems, especially the next event and remaining-time prediction tasks. Nevertheless, designing a deep neural architecture that performs competitively across various tasks is challenging as existing methods fail to capture long-range dependencies in the input sequences and perform poorly for lengthy process traces. In this paper, we propose ProcessTransformer, an approach for learning high-level representations from event logs with an attention-based network. Our model incorporates long-range memory and relies on a self-attention mechanism to establish dependencies between a multitude of event sequences and corresponding outputs. We evaluate the applicability of our technique on nine real event logs. We demonstrate that the transformer-based model outperforms several baselines of prior techniques by obtaining on average above 80% accuracy for the task of predicting the next activity. Our method also perform competitively, compared to baselines, for the tasks of predicting event time and remaining time of a running case.

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Section: Predictive Process Monitoringmentioning

confidence: 99%

ProcessTransformer: Predictive Business Process Monitoring with Transformer Network

Bukhsh,

Saeed,

Dijkman

2021

Preprint

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“…Most of the DNN architectures proposed for the next activity and timestamp prediction in PBPM [17] use "vanilla" LSTM cells [9]. LSTMs belong to the class of recurrent neural networks [11] and are designed to handle temporal dependencies in sequential prediction problems [3].…”

Section: Long Short-term Memory Cellsmentioning

confidence: 99%

Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

Nguyen

Chatterjee

Weinzierl

et al. 2020

Preprint

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Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next activity and timestamp prediction can help to improve the performance of operational business processes. Recently, many PBPM solutions based on deep learning were proposed by researchers. Due to the sequential nature of event log data, a common choice is to apply recurrent neural networks with long short-term memory (LSTM) cells. We argue, that the elapsed time between events is informative. However, current PBPM techniques mainly use "vanilla" LSTM cells and hand-crafted time-related control flow features. To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells. T-LSTM cells incorporate the elapsed time between consecutive events inherently to adjust the cell memory. Furthermore, we introduce costsensitive learning to account for the common class imbalance in event logs. Our experiments on publicly available benchmark event logs indicate the effectiveness of the introduced techniques.

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“…To predict next activities, we use a "vanilla", i.e. basic, long short-term memory network (LSTM) [7] because most of the PBPM techniques for predicting next activities rely on this DNN architecture [24]. LSTMs belong to the class of recurrent neural networks (RNNs) [8] and are designed to handle temporal dependencies in sequential prediction problems [1].…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

“…Many of these techniques are geared to address the next activity prediction task. For that, most of the recent techniques rely on LSTMs [24] such as Weinzierl et al [25]. To predict not only the next activities with a single predictive model, Tax et al [20] suggest a multi-task LSTM-based DNN architecture.…”

Section: Related Workmentioning

confidence: 99%

Prescriptive Business Process Monitoring for Recommending Next Best Actions

Weinzierl,

Dunzer,

Zilker

et al. 2020

Preprint

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Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN's learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique's next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances.

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An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

Cited by 5 publications

References 43 publications

ProcessTransformer: Predictive Business Process Monitoring with Transformer Network

ProcessTransformer: Predictive Business Process Monitoring with Transformer Network

Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

Prescriptive Business Process Monitoring for Recommending Next Best Actions

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