Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

Theis, Julian; Darabi, Houshang

doi:10.1145/3331155

Cited by 9 publications

(14 citation statements)

References 36 publications

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“…The BPIC, as part of the BPM and ICPM conference series, is held on an annual basis. These logs are publicly available through the 4TU.center repository of Eindhoven University, and cover domains like Dutch hospital (BPIC 2011), 55 Volvo IT Belgium process (BPIC 2013), 71 Employee Insurance Agency (BPIC 2016), 63 Dutch financial institute (BPIC 2012, 2017), 55,62,65,66 and purchase order handling process (BPIC 2019) 50 …”

Section: Resultsmentioning

confidence: 99%

A survey on recommendation in process mining

Eili

Rezaeenour

2022

Concurrency and Computation

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Recommender systems have been widely applied in several domains to make informed decisions by recommending items that might be of interest. Considering recommendation during business process execution is also highly advantageous as the efficient suggestions about possible activities or resources can impact process performance. However, the deployment of the recommendation frameworks in process mining still needs more investigations to identify the current challenges to enable the practical application of research findings and ensure a large-scale adoption of this technique. Accordingly, a systematic review is conducted to provide a taxonomy of the published studies on process-aware recommender systems based on specified criteria, including the type and perspective of recommendation, a list of datasets and evaluation metrics used in the setting of PARS, implementation environments, and different algorithms used in PARS. In this regard, there are various insights extracted from this study: (i) Most studies in the business process analysis domain are of descriptive and predictive nature, (ii) recommendation in process mining is an emerging research area that is being evolving; the majority of proposals relate to 2015 and after that, and (iii) due to the lack of common evaluation protocol, datasets, and metrics, most studies are validated through experiments and prototyping, with less tendency to the practical implementation of a solution regarding real scenarios.

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Section: Resultsmentioning

confidence: 99%

A survey on recommendation in process mining

Eili

Rezaeenour

2022

Concurrency and Computation

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“…Other applications can be found in atypical process mining disciplines such as in black box controller logic estimation [71] that has the potential to release huge amounts of engineers from heavy-duty of repeated controller design work. The proposed approach can further be applied in the novel process mining discipline of Human-Computer Interaction [72]. Predicting upcoming user interactions with a given computer system might unveil error-prone or inefficient interfaces and can be used to create accessible and interactive devices to overcome e.g.…”

Section: Discussionmentioning

confidence: 99%

Decay Replay Mining to Predict Next Process Events

Theis

Darabi

2019

IEEE Access

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In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event from a process state. However, such deep learning models by themselves lack a clear representation of the process states. At the same time, recent methods have neglected the time feature of event instances. In this paper, we take advantage of Petri nets as a powerful tool in modeling complex process behaviors considering time as an elemental variable. We propose an approach which starts from a Petri net process model constructed by a process mining algorithm. We enhance the Petri net model with time decay functions to create continuous process state samples. Finally, we use these samples in combination with discrete token movement counters and Petri net markings to train a deep learning model that predicts the next event. We demonstrate significant performance improvements and outperform the state-of-the-art methods on nine real-world benchmark event logs.

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“…Process mining is a comparatively young research discipline that aims to extract knowledge from such sequences. Applications of process mining can be found across many industries, mainly to analyze and optimize applied processes, such as in business process management [5], automation [6], [7], manufacturing [8], [9], and recently in healthcare [10], [11]. Healthcare organizations increasingly acknowledge process mining and the use of empirical data to improve processes [12].…”

Section: Introductionmentioning

confidence: 99%

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

Theis

Galanter

Boyd

et al. 2022

IEEE J. Biomed. Health Inform.

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Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and timeconsuming task due to a large number of influencing factors. Healthcare providers are interested in the detection of ICU patients at higher risk, such that risk factors can possibly be mitigated. While such severity scoring methods exist, they are commonly based on a snapshot of the health conditions of a patient during the ICU stay and do not specifically consider a patient's prior medical history. In this paper, a process mining/deep learning architecture is proposed to improve established severity scoring methods by incorporating the medical history of diabetes patients. First, health records of past hospital encounters are converted to event logs suitable for process mining. The event logs are then used to discover a process model that describes the past hospital encounters of patients. An adaptation of Decay Replay Mining is proposed to combine medical and demographic information with established severity scores to predict the inhospital mortality of diabetes ICU patients. Significant performance improvements are demonstrated compared to established risk severity scoring methods and machine learning approaches using the Medical Information Mart for Intensive Care III dataset.

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Behavioral Petri Net Mining and Automated Analysis for Human-Computer Interaction Recommendations in Multi-Application Environments

Cited by 9 publications

References 36 publications

A survey on recommendation in process mining

A survey on recommendation in process mining

Decay Replay Mining to Predict Next Process Events

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

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