Activity Prediction of Business Process Instances with Inception CNN Models

Mauro, Nicola Di; Appice, Annalisa; Basile, Teresa Maria Altomare

doi:10.1007/978-3-030-35166-3_25

Cited by 57 publications

(45 citation statements)

References 14 publications

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“…The implementation of many architectures requires the input features to be of a fixed size. Hence, the authors cut the traces into fixed-sized prefixes (Evermann et al 2017;Tax et al 2017;Nolle et al 2018;Schoenig et al 2018;Al-Jebrni et al 2018;Di Mauro et al 2019;Camargo et al 2019). For the prediction of events having a shorter prefix, the feature matrix is padded with zeros.…”

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%

“…and Hinkka et al (2019) used Gated Recurrent Units, the majority decides on Long-Short-Term Memory Cells (Al-Jebrni et al 2018;Di Mauro et al 2019;Pasquadibisceglie et al 2019…”

mentioning

confidence: 99%

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

2021

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“…Despite RNN-based architectures being the most popular option for business process prediction, other deep learning techniques are explored by some researches. Convolutional Neural Networks (CNNs) [29,30,31] have been used in process predictions by transferring sequential event data to spatial image-like data for process prediction. The modernistic research by Bukhsh et al [32] introduces a novel transformer framework into predictive process analytics, which replace the RNN cells with attention mechanisms in recurrent neural network.…”

Section: Deep Learning-based Predictive Process Analyticsmentioning

confidence: 99%

Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms

Wickramanayake,

He,

Ouyang

et al. 2021

Preprint

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Predictive process analytics, often underpinned by deep learning techniques, is a newly emerged discipline dedicated to providing business process intelligence in modern organisations. Whilst accuracy has been a dominant criterion in building predictive capabilities, the use of deep learning techniques comes at the cost of the resulting models being used as 'black boxes', i.e., they are unable to provide insights into why a certain business process prediction was made. So far little attention has been paid to supporting interpretability in the design of deep learning-based process predictive models.In this paper, we address the 'black-box' problem in the context of predictive process analytics by developing attention-based models that are capable to inform both what and why is a prediction. We propose i) two types of attentions -event attention to capture the impact of specific events on a prediction, and attribute attention to reveal which attribute(s) of an event influenced the prediction; and ii) two attention mechanisms -shared attention mechanism and specialised attention mechanism to reflect different design decisions between whether to construct attribute attention on individual input features (specialised) or using the concatenated feature tensor of all input feature vectors (shared). These lead to two distinct attentionbased models, and both are interpretable models that incorporate interpretability directly into the structure of a process predictive model. We conduct experimental evaluation of the proposed models using real-life dataset, and comparative analysis between the models for accuracy and interpretability.The results demonstrate that i) the proposed attention-based models can achieve reasonably high accuracy; ii) both are capable of providing relevant interpretations (when validated against domain knowledge); and iii) while the shared attention-based model tends to perform (slightly) better than the specialised attention-based model, the latter tends to provide more relevant interpretations than the former, reflecting the fact that the specialised attention-based model is designed to facilitate better interpretability.

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“…1D CNNs have been recently used in several domains like process mining [48], remote sensing [49], wind prediction [50], medical image processing [19] or malware detection [39]. 1D CNNs process 1-dimensional input vectors, like sequential data, and the filter in the convolution slides along one dimension only.…”

Section: D Convolutional Neural Networkmentioning

confidence: 99%

Multi-Channel Deep Feature Learning for Intrusion Detection

et al. 2020

Self Cite

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Networks had an increasing impact on modern life since network cybersecurity has become an important research field. Several machine learning techniques have been developed to build network intrusion detection systems for correctly detecting unforeseen cyber-attacks at the network-level. For example, deep artificial neural network architectures have recently achieved state-of-the-art results. In this paper a novel deep neural network architecture is defined, in order to learn flexible and effective intrusion detection models, by combining an unsupervised stage for multi-channel feature learning with a supervised one exploiting feature dependencies on cross channels. The aim is to investigate whether class-specific features of the network flows could be learned and added to the original ones in order to increase the model accuracy. In particular, in the unsupervised stage, two autoencoders are separately learned on normal and attack flows, respectively. As the top layer in the decoder of these autoencoders reconstructs samples in the same space as the input one, they could be used to define two new feature vectors allowing the representation of each network flow as a multi-channel sample. In the supervised stage, a multi-channel parametric convolution is adopted, in order to learn the effect of each channel on the others. In particular, as the samples belong to two different distributions (normal and attack flows), the samples labelled as normal should be more similar to the representation reconstructed with the normal autoencoder than that of the attack one, and viceversa. This expected dependency will be exploited to better disentangle the differences between normal and attack flows. The proposed neural network architecture leads to better predictive accuracy when compared to competitive intrusion detection architectures on three benchmark datasets.

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Activity Prediction of Business Process Instances with Inception CNN Models

Cited by 57 publications

References 14 publications

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

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

Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms

Multi-Channel Deep Feature Learning for Intrusion Detection

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