MM-Pred: A Deep Predictive Model for Multi-attribute Event Sequence

Li, Lin; Wen, Lijie; Wang, Jianmin

doi:10.1137/1.9781611975673.14

Cited by 84 publications

(114 citation statements)

References 20 publications

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“…The most recent study in sequence modelling [23] presented an RNN-based modulated model for multi-task prediction of event sequences and event attributes. The model (called MM-Pred) includes an LSTM encoder-decoder and a modulator capable of learning inter-dependencies among event attributes.…”

Section: ) Deep Learning In Process Miningmentioning

confidence: 99%

“…The details of these logs are provided in the next section and Table 4. The accuracy of the next event predictions of the proposed two models was compared to that of three other LSTM models proposed by Tax et al [20], Camargo et al [21] and Lin et al [23]. From the five real-life logs listed above, these studies employed only the Helpdesk and BPIC 2012 logs.…”

Section: A Evaluationmentioning

confidence: 99%

“…Also, Camargo et al [21] used pretrained embedded dimensions in all their experiments. Lin et al [23] used a two-layer LSTM-based encoder-decoder architecture with 32 neurons per layer for each encoder and decoder cell. Random embedding was established for each event.…”

Section: A Evaluationmentioning

confidence: 99%

“…Random embedding was established for each event. Only the S-Pred model proposed by Lin et al [23] was considered in this study as it is concerned with predicting next events based on event sequences alone (i.e. without considering additional information such as event attributes).…”

Section: A Evaluationmentioning

confidence: 99%

“…Road Traffic Fine Management Process [33] -This is a real-life event log of an information system managing road traffic fines. The following experimental protocol was adopted to closely follow the experiments conducted by Tax et al [20], Camargo et al [21] and Lin et al [23] for a fair comparison with their models:…”

Section: B Real-life Event Logsmentioning

confidence: 99%

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A Graph-Based Approach to Interpreting Recurrent Neural Networks in Process Mining

2020

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Process mining is often used by organisations to audit their business processes and improve their services and customer relations. Indeed, process execution (or event) logs constantly generated through various information systems can be employed to derive valuable insights about business operations. Compared to traditional process mining techniques such as Petri nets and the Business Process Model and Notation (BPMN), deep learning methods such as Recurrent Neural Networks, and Long Short-Term Memory (LSTM) in particular, have proven to achieve a better performance in terms of accuracy and generalising ability when predicting next events in business processes. However, unlike the traditional network-based process mining techniques that can be used to visually present the entire discovered process, the existing deep learning-based methods for process mining lack a mechanism explaining how the predictions of next events are made. This study proposes a new approach to process mining by combining the benefits of the earlier, visually explainable graph-based methods and later, more accurate but unexplainable deep learning methods. According to the proposed approach, an LSTM model is employed first to find probabilities for each known event to appear in the process next. These probabilities are then used to generate a visually interpretable process model graph that represents the decision-making process of the LSTM model. The level of detail in this graph can be adjusted using a probability threshold, allowing to address a range of process mining tasks such as business process discovery and conformance checking. The advantages of the proposed approach over existing LSTM-based process mining methods in terms of both accuracy and explainability are demonstrated using real-world event logs.

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Section: ) Deep Learning In Process Miningmentioning

confidence: 99%