A Hybrid Model for Business Process Event Prediction

Le, Mai; Gabrys, Bogdan; Nauck, Detlef

doi:10.1007/978-1-4471-4739-8_13

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…Only five approaches are concerned with predicting the next event (Breuker et al, 2016;Ceci et al, 2014;Lakshmanan et al, 2015;Le et al, 2012;Unuvar et al, 2016), all of which use an explicit model representation such as a state-transition, HMM (hidden Markov models), or PFA (probabilistic finite automatons) model. The MSA approach by Le et al (2012) considers each trace prefix as a state in a state-transition matrix. From the observed prefixes and their next events, a state transition matrix is built.…”

Section: Related Workmentioning

confidence: 99%

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Predicting process behaviour using deep learning

Evermann

Rehse

Fettke

2017

Decision Support Systems

311

284

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Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

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Section: Related Workmentioning

confidence: 99%

“…Evaluating the approach on two datasets from a telecommunications company, Le et al (2012) report accuracies in predicting the next event of up to 25% and 70% for their two datasets.…”

Section: Related Workmentioning

confidence: 99%

Predicting process behaviour using deep learning

Evermann

Rehse

Fettke

2017

Decision Support Systems

311

284

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“…Markov classifier. Markov Models are commonly used in process mining [24,[31][32][33]63], where they are primarily used for predicting objectives like the remaining time or the next event, and not for sequence classification. It can also be used for classification though, as Markov Models represent the data of each class in the training data as a Markov process.…”

Section: Learning Methodsmentioning

confidence: 99%

“…The verification of linear temporal logic (LTL) compliance is approached in [27] by using Decision Trees, which is similar to the research of [28], which uses Multi Layer Perceptrons (MLP) for detecting service level agreement violations. One large research topic is also the prediction of the next events during runtime, for which graph theory [25], LSTMs [29], Decision Trees [30][31][32], Markov Classifiers [31][32][33] or Multi Layer Perceptrons [34] are used.…”

Section: Related Workmentioning

confidence: 99%

Features spaces and a learning system for structural-temporal data, and their application on a use case of real-time communication network validation data

Schwenk¹,

Jochinke²,

Müller³

2020

PLoS ONE

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The service quality and system dependability of real-time communication networks strongly depends on the analysis of monitored data, to identify concrete problems and their causes. Many of these can be described by either their structural or temporal properties, or a combination of both. As current research is short of approaches sufficiently addressing both properties simultaneously, we propose a new feature space specifically suited for this task, which we analyze for its theoretical properties and its practical relevance. We evaluate its classification performance when used on real-world data sets of structural-temporal mobile communication data, and compare it to the performance achieved of feature representations used in related work. For this purpose we propose a system which allows the automatic detection and prediction of classes of pre-defined sequence behavior, greatly reducing costs caused by the otherwise required manual analysis. With our proposed feature spaces this system achieves a precision of more than 93% at recall values of 100%, with an up to 6.7% higher effective recall than otherwise similarly performing alternatives, notably outperforming alternative deep learning, kernel learning and ensemble learning approaches of related work. Furthermore the supported system calibration allows separating reliable from unreliable predictions more effectively, which is highly relevant for any practical application.

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“…Most works use an explicit process model representation, such as the Hidden Markov Model (HMM). Le et al [30] propose hybrid Markov models for predicting the next step in a process instance. If an instance reaches an unknown state, the model results in the prediction based on the most similar state by applying edit distance.…”

Section: Predictive Business Process Monitoringmentioning

confidence: 99%

Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

Park

Song

2019

2019 International Conference on Process Mining (ICPM)

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Predictive business process monitoring aims at providing predictions about running instances by analyzing logs of completed cases in a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates the offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using Bayesian Neural Networks (BNNs) with the online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.

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A Hybrid Model for Business Process Event Prediction

Cited by 19 publications

References 10 publications

Predicting process behaviour using deep learning

Predicting process behaviour using deep learning

Features spaces and a learning system for structural-temporal data, and their application on a use case of real-time communication network validation data

Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

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