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Process Mining and Its Applications

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 23776

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Special Issue Editors

Prof. Dr. Sally McClean

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Guest Editor

School of Computing, Ulster University, Jordanstown, Newtownabbey Co. Antrim BT37 0QB, UK
Interests: data mining; process mining; Markov models; pervasive computing; optimization; statistics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Adele Marshall

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Guest Editor

1. School of Mathematics and Physics, Queen's University Belfast, Northern Ireland, BT7 1NN, UK
2. Adjunct Professor, Faculty of Business and Information Technology, Ontario Tech University, Ontario K7L 3N6, Canada
Interests: process Mining; real-time analytics; Markov models; survival techniques; data mining; benchmark analysis; predictive models; statistical modelling

Dr. Ian McChesney

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Guest Editor

School of Computing, Ulster University, Jordanstown, Newtownabbey Co. Antrim, BT37 0QB, UK
Interests: process modelling; program comprehension; human activity recognition; open data
Special Issues, Collections and Topics in MDPI journals

Dr. Zeeshan Tariq

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Guest Editor

Faculty of Computing, Engineering and The Built Environment, Ulster University, Belfast BT15 1ED, UK
Interests: business analytics; process mining; machine learning

Special Issue Information

Dear Colleagues,

Process mining is a research field aimed at developing algorithms and methodologies to extract useful knowledge from event data. Process mining methods have been successfully applied to logs of business process execution recorded by transactional IT systems, with the ultimate goal of analyzing and improving organizational productivity along such performance dimensions as efficiency, quality, compliance, and risk. Moreover, such methods are increasingly being used—with an interdisciplinary perspective—in other application domains beyond those related to business processes, such as in the context of distributed ledger technologies (DLT), robotic process automation (RPA), and the Internet of Things (IoT). This Special Issue aims to provide a high-quality forum in which interdisciplinary researchers and practitioners can exchange research findings and ideas on process mining and its applications.

We invite you to submit research to this Special Issue on “Process Mining and Emerging Applications” on subjects covering the whole range from theory to applications. The following is a (non-exhaustive) list of topics of interest:

Process mining techniques;
Automated discovery of process models;
Conformance/compliance analysis;
Multiperspective process mining;
Predictive process analytics;
Prescriptive process analytics and recommender systems;
Privacy-preserving process mining;
Visual process analytics;
Mining from non-process-aware systems/event streams.

We welcome applications and case studies in:

Distributed ledger technologies (DLT);
(Cyber)security and privacy;
Risk management;
Robotic process automation (RPA);
Sensors, Internet-of-Things (IoT), and wearable devices;
Specific domains such as accounting, finance, government, healthcare, software engineering and manufacturing.

Finally, I would like to thank Mr. Zeeshan Tariq for his valuable work in assisting me with this Special Issue.

Prof. Dr. Sally McClean
Prof. Dr. Adele Marshall
Dr. Ian McChesney
Dr. Zeeshan Tariq
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

process mining algorithms
conformance/compliance analysis
process analytics
event logs analysis.

Published Papers (10 papers)

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Research

60 pages, 11060 KiB

Open AccessArticle

An Experimental Outlook on Quality Metrics for Process Modelling: A Systematic Review and Meta Analysis

by Ashish T. S. Ireddy and Sergey V. Kovalchuk

Algorithms 2023, 16(6), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/a16060295 - 10 Jun 2023

Viewed by 1760

Abstract

The ideology behind process modelling is to visualise lengthy event logs into simple representations interpretable to the end user. Classifying process models as simple or complex is based on criteria that evaluate attributes of models and quantify them on a scale. These metrics measure various characteristics of process models and describe their qualities. Over the years, vast amounts of metrics have been proposed in the community, making it difficult to find and select the appropriate ones for implementation. This paper presents a state-of-the-art meta-review that lists and summarises all the evaluation metrics proposed to date. We have studied the behaviour of the four most widely used metrics in process mining with an experiment. Further, we have used seven healthcare domain datasets of varying natures to analyse the behaviour of these metrics under different threshold conditions. Our work aims to propose and demonstrate the capabilities to use our selected metrics as a standard of measurement for the process mining domain. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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33 pages, 7828 KiB

Open AccessArticle

Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains

by Zhi Chen, Shuai Zhang, Sally McClean, Fionnuala Hart, Michael Milliken, Brahim Allan and Ian Kegel

Algorithms 2023, 16(2), 82; https://0-doi-org.brum.beds.ac.uk/10.3390/a16020082 - 02 Feb 2023

Cited by 1 | Viewed by 2314

Abstract

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed ‘most likely trajectory’ and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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24 pages, 4724 KiB

Open AccessArticle

Phase-Type Survival Trees to Model a Delayed Discharge and Its Effect in a Stroke Care Unit

by Lalit Garg, Sally McClean, Brian Meenan, Maria Barton, Ken Fullerton, Sandra C. Buttigieg and Alexander Micallef

Algorithms 2022, 15(11), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/a15110414 - 05 Nov 2022

Viewed by 1753

Abstract

The problem of hospital patients’ delayed discharge or ‘bed blocking’ has long been a challenge for healthcare managers and policymakers. It negatively affects the hospital performance metrics and has other severe consequences for the healthcare system, such as affecting patients’ health. In our previous work, we proposed the phase-type survival tree (PHTST)-based analysis to cluster patients into clinically meaningful patient groups and an extension of this approach to examine the relationship between the length of stay in hospitals and the destination on discharge. This paper describes how PHTST-based clustering can be used for modelling delayed discharge and its effects in a stroke care unit, especially the extra beds required, additional cost, and bed blocking. The PHTST length of stay distribution of each group of patients (each PHTST node) is modelled separately as a finite state continuous-time Markov chain using Coxian-phase-type distributions. Delayed discharge patients waiting for discharge are modelled as the Markov chain, called the ‘blocking state’ in a special state. We can use the model to recognise the association between demographic factors and discharge delays and their effects and identify groups of patients who require attention to resolve the most common delays and prevent them from happening again. The approach is illustrated using five years of retrospective data of patients admitted to the Belfast City Hospital with a stroke diagnosis. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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25 pages, 6475 KiB

Open AccessArticle

Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis

by Zeeshan Tariq, Darryl Charles, Sally McClean, Ian McChesney and Paul Taylor

Algorithms 2022, 15(8), 257; https://0-doi-org.brum.beds.ac.uk/10.3390/a15080257 - 25 Jul 2022

Cited by 1 | Viewed by 1634

Abstract

A significant challenge for organisations is the timely identification of the abnormalities or deviations in their process executions. Abnormalities are generally due to missing vital aspects of a process or possession of unwanted behaviour in the process execution. Conformance analysis techniques examine the synchronisation between the recorded logs and the learned process models, but the exploitation of event logs for abnormality detection is a relatively under-explored area in process mining. In this paper, we proposed a novel technique for the identification of abnormalities in business process execution through the extension of available conformance analysis techniques. Non-traditional conformance analysis techniques are used to find correlations and discrepancies between simulated and observed behaviour in process logs. Initially, the raw event log is filtered into two variants, successful and failed, based upon the outcome of the instances. Successfully executed instances refer to an ideal conduct of process and are utilised to discover an optimal process model. Later, the process model is used as a behavioural benchmark to classify the abnormality in the failed instances. Abnormal behaviour is compiled grounded on three dimensions of conformance, control flow-based alignment, trace-level alignment and event-level alignment. For early predictions, we introduced the notion of conformance lifeline presenting the impact of varying fitness scores during process execution. We applied the proposed methodology to a real-world event log and presented several process-specific improvement measures in the discussion section. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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29 pages, 3889 KiB

Open AccessArticle

XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework

by Ghada El-khawaga, Mervat Abu-Elkheir and Manfred Reichert

Algorithms 2022, 15(6), 199; https://0-doi-org.brum.beds.ac.uk/10.3390/a15060199 - 08 Jun 2022

Cited by 5 | Viewed by 2397

Abstract

Predictive Process Monitoring (PPM) has been integrated into process mining use cases as a value-adding task. PPM provides useful predictions on the future of the running business processes with respect to different perspectives, such as the upcoming activities to be executed next, the final execution outcome, and performance indicators. In the context of PPM, Machine Learning (ML) techniques are widely employed. In order to gain trust of stakeholders regarding the reliability of PPM predictions, eXplainable Artificial Intelligence (XAI) methods have been increasingly used to compensate for the lack of transparency of most of predictive models. Multiple XAI methods exist providing explanations for almost all types of ML models. However, for the same data, as well as, under the same preprocessing settings or same ML models, generated explanations often vary significantly. Corresponding variations might jeopardize the consistency and robustness of the explanations and, subsequently, the utility of the corresponding model and pipeline settings. This paper introduces a framework that enables the analysis of the impact PPM-related settings and ML-model-related choices may have on the characteristics and expressiveness of the generated explanations. Our framework provides a means to examine explanations generated either for the whole reasoning process of an ML model, or for the predictions made on the future of a certain business process instance. Using well-defined experiments with different settings, we uncover how choices made through a PPM workflow affect and can be reflected through explanations. This framework further provides the means to compare how different characteristics of explainability methods can shape the resulting explanations and reflect on the underlying model reasoning process. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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15 pages, 1894 KiB

Open AccessArticle

Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models

by Adele H. Marshall and Aleksandar Novakovic

Algorithms 2022, 15(6), 196; https://0-doi-org.brum.beds.ac.uk/10.3390/a15060196 - 07 Jun 2022

Viewed by 1589

Abstract

As the world moves into the exciting age of Healthcare 4.0, it is essential that patients and clinicians have confidence and reassurance that the real-time clinical decision support systems being used throughout their care guarantee robustness and optimal quality of care. However, current systems involving autonomic behaviour and those with no prior clinical feedback, have generally to date had little focus on demonstrating robustness in the use of data and final output, thus generating a lack of confidence. This paper wishes to address this challenge by introducing a new process mining approach based on a statistically robust methodology that relies on the utilisation of conditional survival models for the purpose of evaluating the performance of Healthcare 4.0 systems and the quality of the care provided. Its effectiveness is demonstrated by analysing the performance of a clinical decision support system operating in an intensive care setting with the goal to monitor ventilated patients in real-time and to notify clinicians if the patient is predicted at risk of receiving injurious mechanical ventilation. Additionally, we will also demonstrate how the same metrics can be used for evaluating the patient quality of care. The proposed methodology can be used to analyse the performance of any Healthcare 4.0 system and the quality of care provided to the patient. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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21 pages, 3003 KiB

Open AccessArticle

Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

by Najah Mary El-Gharib and Daniel Amyot

Algorithms 2022, 15(6), 180; https://0-doi-org.brum.beds.ac.uk/10.3390/a15060180 - 25 May 2022

Cited by 2 | Viewed by 2725

Abstract

Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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19 pages, 3614 KiB

Open AccessArticle

Detecting and Responding to Concept Drift in Business Processes

by Lingkai Yang, Sally McClean, Mark Donnelly, Kevin Burke and Kashaf Khan

Algorithms 2022, 15(5), 174; https://0-doi-org.brum.beds.ac.uk/10.3390/a15050174 - 21 May 2022

Cited by 2 | Viewed by 2019

Abstract

Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data. Due to factors such as seasonal effects and policy updates, concept drifts can occur in customer transitions and time spent throughout the process, either suddenly or gradually. In a concept drift context, we can discard the old data and retrain the model using new observations (sudden drift) or combine the old data with the new data to update the model (gradual drift) or maintain the model as unchanged (no drift). In this paper, we model a response to concept drift as a sequential decision making problem by combing a hierarchical Markov model and a Markov decision process (MDP). The approach can detect concept drift, retrain the model and update customer profiles automatically. We validate the proposed approach on 68 artificial datasets and a real-world hospital billing dataset, with experimental results showing promising performance. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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19 pages, 4499 KiB

Open AccessArticle

Integrating Process Mining with Discrete-Event Simulation for Dynamic Productivity Estimation in Heavy Civil Construction Operations

by Khandakar M. Rashid and Joseph Louis

Algorithms 2022, 15(5), 173; https://0-doi-org.brum.beds.ac.uk/10.3390/a15050173 - 21 May 2022

Cited by 5 | Viewed by 2510

Abstract

Construction companies are increasingly utilizing sensing technologies to automatically record different steps of the construction process in detail for effective monitoring and control. This generates a significant amount of event data that can be used to learn the underlying behavior of agents in a construction site using process mining. While process mining can be used to discover the real process and identify and analyze deviations and bottlenecks in operations, it is a backward-looking approach. On the other hand, discrete event simulation (DES) provides a means to forecast future performance from historical data to enable proactive decision-making by operation managers relating to their projects. However, this method is largely unused by the industry due to the specialized knowledge required to create the DES models. This paper thus proposes a framework that extends the utility of collecting event data and their process models, by transforming them into DES models for forecasting future performance. This framework also addresses another challenge of using DES relating to its inability to update itself as the project progresses. This challenge is addressed by using the Bayesian updating technique to continuously update the input parameters of the simulation model for the most up-to-date estimation based on data collected from the field. The proposed framework was validated on a real-world case study of an earthmoving operation. The results show that the process mining techniques could accurately discover the process model from the event data collected from the field. Furthermore, it was noted that continuous updating of DES model input parameters can provide accurate and reliable productivity estimates based on the actual data generated from the field. The proposed framework can help stakeholders to discover the underlying sequence of their operations, and enable timely, data-driven decisions regarding operations control. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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21 pages, 3240 KiB

Open AccessArticle

A Statistical Approach to Discovering Process Regime Shifts and Their Determinants

by Atiq W. Siddiqui and Syed Arshad Raza

Algorithms 2022, 15(4), 127; https://0-doi-org.brum.beds.ac.uk/10.3390/a15040127 - 13 Apr 2022

Cited by 2 | Viewed by 2608

Abstract

Systematic behavioral regime shifts inevitably emerge in real-world processes in response to various determinants, thus resulting in temporally dynamic responses. These determinants can be technical, such as process handling, design, or policy elements; or environmental, socio-economic or socio-technical in nature. This work proposes a novel two-stage methodology in which the first stage involves statistically identifying and dating all regime shifts in the time series process event logs. The second stage entails identifying contender determinants, which are statistically and temporally evaluated for their role in forming new behavioral regimes. The methodology is general, allowing varying process evaluation bases while putting minimal restrictions on process output data distribution. We demonstrated the efficacy of our approach via three cases of technical, socio-economic and socio-technical nature. The results show the presence of regime shifts in the output logs of these cases. Various determinants were identified and analyzed for their role in their formation. We found that some of the determinants indeed caused specific regime shifts, whereas others had no impact on their formation. Full article

(This article belongs to the Special Issue Process Mining and Its Applications)

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