Specification-Driven Multi-perspective Predictive Business Process Monitoring

Santoso, Ario

doi:10.1007/978-3-319-91704-7_7

Cited by 4 publications

(6 citation statements)

References 32 publications

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“…In our implementation, to get the expected quality of predictions, the users are allowed to choose the desired classification/regression model as well as the feature encoding mechanisms (in order to allow some sort of feature engineering). This article extends [57] in several ways. First, we extend the specification language so as to incorporate various aggregate functions such as Max, Min, Average, Sum, Count, and Concat.…”

Section: Introductionmentioning

confidence: 68%

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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Section: Introductionmentioning

confidence: 68%

Specification-driven predictive business process monitoring

Santoso

Felderer

2019

Softw Syst Model

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“…We proceed this validation by comparing the performance of ORANGE to that of the list of competitors -based on SVM, LR, RF and XGB -introduced in [5], 7 as well as the competitor -based on LSTM -described in [25]. 8 SVM, LR, RF and XGB are run using the optimization described in [5] to select the hyperparameters of the machine learning algorithms. In addition, similarly to ORANGE, they use the Aggregation schema for the trace feature extraction.…”

Section: Resultsmentioning

confidence: 99%

“…The prediction of the correct class is based on a classification model extracted from historical process execution logs (event logs) [8]. In particular, current methods for outcome-oriented predictive process mainly follow the standard pipeline for classification involving two phases: (i) a feature extraction phase where features are extracted from event traces so that each trace is abstracted as a vector of features representing event occurrences and (ii) a classifier construction phase, where feature vectors are given as input to a machine learning method (e.g.…”

Section: Introductionmentioning

confidence: 99%

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ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

et al. 2020

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The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully. INDEX TERMS Predictive process analytics; Outcome prediction; Computer vision, Convolutional neural networks; Spatial data modeling.

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“…Furthermore, it categorizes approaches in the literature based on their process-awareness, i.e., whether or not an approach employs an explicit representation of process models. Santoso [12] specified a language for properly defining the prediction task, enabling researchers to express various types of predictive monitoring problems while not relying on any particular machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs

Tama

Comuzzi

2022

Electronics

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Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article, an advanced stacking ensemble technique for outcome-based predictive monitoring is introduced. The proposed stacking ensemble employs strong learners as base classifiers, i.e., other ensembles. More specifically, we consider stacking of random forests, extreme gradient boosting machines, and gradient boosting machines to train a process outcome prediction model. We evaluate the proposed approach using publicly available event logs. The results show that the proposed model is a promising approach for the outcome-based prediction task. We extensively compare the performance differences among the proposed methods and the base strong learners, using also statistical tests to prove the generalizability of the results obtained.

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Specification-Driven Multi-perspective Predictive Business Process Monitoring

Cited by 4 publications

References 32 publications

Specification-driven predictive business process monitoring

Specification-driven predictive business process monitoring

ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs

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