Predictive Business Process Monitoring – Remaining Time Prediction using Deep Neural Network with Entity Embedding

Wahid, Nur Ahmad; Adi, Taufik Nur; Bae, Hyerim; Choi, Yongki

doi:10.1016/j.procs.2019.11.219

Cited by 22 publications

(18 citation statements)

References 2 publications

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“…Eleven studies fell back on one-hot-encoding, and another 11 studies used embedding. Wahid et al (2019) directly compared one-hot-encoding to embedding in their implementation and found that embedding leads to increased performance. Tax et al (2017) observed better performance with their one-hot-encoding solution compared to the embedding approach of Evermann et al (2017).…”

Section: Data Preprocessing and Feature Engineeringmentioning

confidence: 99%

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A systematic literature review on state-of-the-art deep learning methods for process prediction

2021

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Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.

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Section: Data Preprocessing and Feature Engineeringmentioning

confidence: 99%

“…the coefficient of determination. The regression results of Bandis et al (2018); Wahid et al (2019) are not shown, since their evaluation was done on individual datasets with time scales, several magnitudes smaller.…”

Section: Prediction Targetmentioning

confidence: 99%

A systematic literature review on state-of-the-art deep learning methods for process prediction

2021

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“…As far as the attributes of the log are concerned, it is possible that not every attribute adds significant information to the predictive problem [43], [44], [48], [49], [51], [52], [53], [54], [56], [58] . [51] trains a predictive model where an alignment weight vector learns the importance of each attribute.…”

Section: Input Datamentioning

confidence: 99%

“…Note that, as shown in TABLE 3 this is the only approach that distinguishes the resources from other attributes of the event log. As far as the approaches that use time features [24], [25], [46], [50], [53], [54], [55], [57], they face the problem of a high variability in the time between the events so these time features may complicate the training phase.…”

Section: Input Datamentioning

confidence: 99%

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Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

Rama-Maneiro¹,

Vidal²,

Lama³

2020

Preprint

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Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

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