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Journal articles on the topic 'Process mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 September 2024

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1

van der Aalst, Wil. "Process mining." ACM SIGKDD Explorations Newsletter 13, no. 2 (May 2012): 45–49. http://dx.doi.org/10.1145/2207243.2207251.

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Accorsi, Rafael, Meike Ullrich, and Wil M. P. van der Aalst. "Process Mining." Informatik-Spektrum 35, no. 5 (August 30, 2012): 354–59. http://dx.doi.org/10.1007/s00287-012-0641-4.

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van der Aalst, Wil. "Process Mining." ACM Transactions on Management Information Systems 3, no. 2 (July 2012): 1–17. http://dx.doi.org/10.1145/2229156.2229157.

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Van Der Aalst, Wil. "Process mining." Communications of the ACM 55, no. 8 (August 2012): 76–83. http://dx.doi.org/10.1145/2240236.2240257.

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Il-Agure, Zakea, and Hicham Noureddine Itani. "Link Mining Process." International Journal of Data Mining & Knowledge Management Process 7, no. 3 (May 30, 2017): 45–51. http://dx.doi.org/10.5121/ijdkp.2017.7304.

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Deneckère, Rebecca, Charlotte Hug, Ghazaleh Khodabandelou, and Camille Salinesi. "Intentional Process Mining." International Journal of Information System Modeling and Design 5, no. 4 (October 2014): 22–47. http://dx.doi.org/10.4018/ijismd.2014100102.

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Understanding people's goals is a challenging issue that is met in many different areas such as security, sales, information retrieval, etc. Intention Mining aims at uncovering intentions from observations of actual activities. While most Intention Mining techniques proposed so far focus on mining individual intentions to analyze web engine queries, this paper proposes a generic technique to mine intentions from activity traces. The proposed technique relies on supervised learning and generates intentional models specified with the Map formalism. The originality of the contribution lies in the demonstration that it is actually possible to reverse engineer the underlying intentional plans built by people when in action, and specify them in models e.g. with intentions at different levels, dependencies, links with other concepts, etc. After an introduction on intention mining, the paper presents the Supervised Map Miner Method and reports two controlled experiments that were undertaken to evaluate precision, recall and F-Score. The results are promising since the authors were able to find the intentions underlying the activities as well as the corresponding map process model with satisfying accuracy, efficiency and performance.
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Pourmasoumi, Asef, and Ebrahim Bagheri. "Business process mining." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630004. http://dx.doi.org/10.1142/s2425038416300044.

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One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.
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Scherwitz, Philipp, Steffen Ziegler, and Johannes Schilp. "Process Mining in der additiven Auftragsabwicklung/Process Mining for additive manufacturing." wt Werkstattstechnik online 110, no. 06 (2020): 429–34. http://dx.doi.org/10.37544/1436-4980-2020-06-69.

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Die Fähigkeit der additiven Fertigung in Losgröße 1 zu fertigen, erzeugt eine hohe Komplexität in der Auftragsabwicklung. Dies stellt die datenbasierte Optimierung der Prozessabläufe vor große Herausforderungen. Durch die geringen Stückzahlen, bei einer hohen Variantenanzahl, ist die Prozessaufnahme in der additiven Fertigung mit signifikanten Aufwänden verbunden. Abhilfe kann hier eine automatisierte Prozessaufnahme schaffen. Deshalb soll in diesem Beitrag die Technologie des Process Mining untersucht und darauf aufbauend eine Vorgehensweise für die datenbasierte Optimierung in der additiven Fertigung vorgestellt werden.   The capability of additive manufacturing to produce in batch size 1 creates a high degree of complexity in order processing. This creates great challenges for the data-based optimization of process flows. Due to the low number of pieces, with a high number of variants, the process recording in additive manufacturing is connected with significant expenditures. This can be overcome by automated process recording. Therefore, this article will examine the technology of process mining and, based on this, present a procedure for data-based optimization in additive manufacturing.
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Vasiliev, A. A., and A. V. Goryachev. "Applying Process Mining to Process Management." LETI Transactions on Electrical Engineering & Computer Science 16, no. 3 (2023): 52–59. http://dx.doi.org/10.32603/2071-8985-2023-16-3-52-59.

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Deals with the intellectual analysis of processes (Process Mining), which has recently gained popularity in various organizations. It is based on the construction of business process models in a specific area (for example, in the field of project management) based on event logs, providing a more accurate understanding of the actions occurring in business processes for the purpose of their subsequent analysis and improvement. The article defines process mining, event logs, lists the main tasks, algorithms and view models. The authors propose a methodology that can be used in the application of process analysis in the field of project management. The authors also highlight the main business processes in project management, for which it is advisable to build models and analyze them.
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ÇELİK, Ufuk, and Eyüp AKÇETİN. "Process Mining Tools Comparison." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 97–104. http://dx.doi.org/10.5824/1309-1581.2018.4.007.x.

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Process mining is a new era in the science of data mining and is a subset of business intelligence. Process mining analysis provides an idea about a general process by comparing each process with others in the terms of time and responsible people who deal with the process. For this reason, event logs are checked. Event logs consist of large data. Because the event logs keep all the records that occur during short time intervals. Special programs are needed to examine such data. These programs generate a process map using information such as event ID, activity, time and responsible person. Through the analysis, processes are discovered, monitored and improved. In this study, the tools named ProM, Disco, Celonis and My-Invenio used in process mining were examined and their performance according to usage features compared. According to the obtained results, the usefulness, performance and reporting features of the software used in a process analysis are revealed.
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Schuh, Günther, Jan-Philipp Prote, Andreas Gützlaff, Sven Cremer, and Seth Schmitz. "Process Mining im Prototypenbau." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 114, no. 11 (November 28, 2019): 707–10. http://dx.doi.org/10.3139/104.112186.

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Lakshmanan, Geetika T., and Rania Khalaf. "Leveraging Process-Mining Techniques." IT Professional 15, no. 5 (September 2013): 22–30. http://dx.doi.org/10.1109/mitp.2012.88.

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Thaler, Tom, Peter Fettke, and Peter Loos. "Process Mining — Fallstudie leginda.de." HMD Praxis der Wirtschaftsinformatik 50, no. 5 (October 2013): 56–65. http://dx.doi.org/10.1007/bf03340853.

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Mannhardt, Felix, Agnes Koschmider, Nathalie Baracaldo, Matthias Weidlich, and Judith Michael. "Privacy-Preserving Process Mining." Business & Information Systems Engineering 61, no. 5 (August 15, 2019): 595–614. http://dx.doi.org/10.1007/s12599-019-00613-3.

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Tax, Niek, Natalia Sidorova, Reinder Haakma, and Wil M. P. van der Aalst. "Mining local process models." Journal of Innovation in Digital Ecosystems 3, no. 2 (December 2016): 183–96. http://dx.doi.org/10.1016/j.jides.2016.11.001.

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van der Aalst, Wil M. P., Hajo A. Reijers, and Laura Maruster. "Process mining beyond workflows." Computers in Industry 161 (October 2024): 104126. http://dx.doi.org/10.1016/j.compind.2024.104126.

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Bauer, Janina. "Process Mining unterstützt Lieferkettenmanagement." VDI-Z 166, no. 07-08 (2024): 53–55. http://dx.doi.org/10.37544/0042-1766-2024-07-08-53.

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Das EU-Lieferkettengesetz bringt weitreichende Verpflichtungen für Unternehmen mit sich. Zugleich bietet es Chancen für mehr Nachhaltigkeit und Effizienz. „Process Mining“ erweist sich dabei als Schlüsseltechnologie, um die komplexen Anforderungen zu meistern und gleichzeitig Wettbewerbsvorteile zu erzielen.
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Joo, Woo-Min, and Jin Young Choi. "Curriculum Mining Analysis Using Clustering-Based Process Mining." Journal of Society of Korea Industrial and Systems Engineering 38, no. 4 (December 30, 2015): 45–55. http://dx.doi.org/10.11627/jkise.2015.38.4.45.

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Demushkina, K. M., and A. V. Kuzmin. "ANALYSIS OF THE CAPABILITIES OF TOOLS FOR IMPLEMENTING PROCESS MINING TECHNOLOGY." Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 25, no. 4 (2023): 114–20. http://dx.doi.org/10.37313/1990-5378-2023-25-4-114-120.

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Modern realities dictate the requirements for effective optimization of production. Modernization of business, economics, production, healthcare and medicine in general is an urgent task of our time. One of the technologies for effective process analysis is process mining. Process mining technology allows you to extract knowledge from an event log, forming a process model based on it. Such tools make it possible to identify ineffective management structures and business processes, malfunctions in software, and incompetence of employees, solving many problems of organizing effective management in various industries. Today there are many software tools that implement process analysis methods: Disco, Apromore, ProM. Celonis Process Mining, UiPath Process Mining, etc. Selecting software for certain tasks is a complex process, which subsequently affects the implementation of the project as a whole. Therefore, it is extremely important to comprehensively analyze all the capabilities and limitations of existing process mining tools. The following criteria were selected: cost of the framework; availability of source code; programming language; the ability to build a decision tree based on the process; technical support; technical documentation; developed community of developers; the ability to create and add plugins; experience with the framework. Healthcare was chosen as the target application area. According to the results of the analysis, it was concluded that the use of the ProM framework is effective for solving problems in the field of healthcare.
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Jung, Chanyil, and Hoojin Lee. "A Study on the Process Mining Technology-Based Process Innovation Methodology." Journal of Korean Institute of Information Technology 17, no. 1 (January 31, 2019): 1–9. http://dx.doi.org/10.14801/jkiit.2019.17.1.1.

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Shershakov, Sergey Andreevich. "VTMine Framework as Applied to Process Mining Modeling." International Journal of Computer and Communication Engineering 4, no. 3 (2015): 166–79. http://dx.doi.org/10.17706/ijcce.2015.4.3.166-179.

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22

Erdogan, Tugba Gurgen, and Ayca Kolukisa Tarhan. "Multi-perspective process mining for emergency process." Health Informatics Journal 28, no. 1 (January 2022): 146045822210771. http://dx.doi.org/10.1177/14604582221077195.

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Background Multi-perspective process mining is an analytical approach that uses data to gain objective insights and uncover hidden problems in business processes from multiple perspectives. Objective In this paper, we apply multi-perspective process mining techniques in the emergency process through a goal-driven performance evaluation method in order to understand and diagnose the timeliness of the emergency process. Methods Unstructured and multi-disciplinary emergency data is analyzed by following Goal-Question-Feature-Indicator (GQFI) method. In this paper, the GQFI method is extended with perspectives, and the insights in the enriched event data are examined by a decision tree model. All of them are applied in a systematic way in relation to the goal of assessing and improving the emergency process in a university hospital. Results We detected the deviations (e.g., skipping the triage and consultation request steps) and two bottlenecks in the emergency process. Among the suggestions for improving the process, are performing defensive medicine in a harmless manner, classification of the emergency services, ensuring triage step is applied to all patients and effective usage of the call system application in consultation activities. Conclusion: The results of this study showed that goal-oriented multi-perspective process mining is effective in identifying process improvements in emergency services.
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Ji-seok Park and 정재윤. "Analysis of Purchase Process Using Process Mining." Korea Journal of BigData 3, no. 1 (August 2018): 47–54. http://dx.doi.org/10.36498/kbigdt.2018.3.1.47.

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van der Aalst, Wil. "Spreadsheets for business process management." Business Process Management Journal 24, no. 1 (February 2, 2018): 105–27. http://dx.doi.org/10.1108/bpmj-10-2016-0190.

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Purpose Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers. Design/methodology/approach The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Findings Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given. Practical implications Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations. Originality/value The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.
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Er, Mahendrawathi, Hanim Maria Astuti, and Dita Pramitasari. "Modeling and Analysis of Incoming Raw Materials Business Process: A Process Mining Approach." International Journal of Computer and Communication Engineering 4, no. 3 (2015): 196–203. http://dx.doi.org/10.17706/ijcce.2015.4.3.196-203.

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Ziegler, Steffen, Stefan Braunreuther, and Gunther Reinhart. "Process Mining zur dynamischen Wertstromaufnahme." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 114, no. 6 (June 27, 2019): 327–31. http://dx.doi.org/10.3139/104.112093.

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Flack, Christian, Simon Dreher, Alexander Birk, and Yannick Wilhelm. "Process Mining in der Produktion." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 115, no. 11 (November 27, 2020): 829–33. http://dx.doi.org/10.3139/104.112459.

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Garg, Neha, and Sonali Agarwal. "Process Mining for Clinical Workflows." International Journal of IT-based Public Health Management 2, no. 2 (July 30, 2015): 17–10. http://dx.doi.org/10.21742/ijiphm.2015.2.2.01.

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Vijayarani, S., A. Sakila, and R. Ramya. "Process Mining - A Comprehensive Review." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 1108–13. http://dx.doi.org/10.26438/ijcse/v6i7.11081113.

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Devi, K. Lalitha, and M. Suryakala. "Educational Process Mining-Different Perspectives." IOSR Journal of Computer Engineering 16, no. 1 (2014): 57–60. http://dx.doi.org/10.9790/0661-16125760.

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Meddah, Ishak, and Belkadi Khaled. "Discovering Patterns using Process Mining." International Journal of Rough Sets and Data Analysis 3, no. 4 (October 2016): 21–31. http://dx.doi.org/10.4018/ijrsda.2016100102.

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Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or their regular expression; The final model is the combination of only two types of small patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Secondly, the authors compute these patterns in parallel, and then combine those small patterns using the composition rules, they have two parties the first is the mine, they discover patterns from execution traces and the second is the combination of these small patterns. The patterns mining and the composition is illustrated by the automaton existing techniques. The Execution traces are the different actions effected by users in the SKYPE and VIBER. The results are general and precise. It minimizes the execution time and the loss of information.
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Chiariello, Francesco, Fabrizio Maria Maggi, and Fabio Patrizi. "ASP-Based Declarative Process Mining." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5539–47. http://dx.doi.org/10.1609/aaai.v36i5.20493.

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We put forward Answer Set Programming (ASP) as a solution approach for three classical problems in Declarative Process Mining: Log Generation, Query Checking, and Conformance Checking. These problems correspond to different ways of analyzing business processes under execution, starting from sequences of recorded events, a.k.a. event logs. We tackle them in their data-aware variant, i.e., by considering events that carry a payload (set of attribute-value pairs), in addition to the performed activity, specifying processes declaratively with an extension of linear-time temporal logic over finite traces (LTLf). The data-aware setting is significantly more challenging than the control-flow one: Query Checking is still open, while the existing approaches for the other two problems do not scale well. The contributions of the work include an ASP encoding schema for the three problems, their solution, and experiments showing the feasibility of the approach.
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Jans, Mieke, Jochen De Weerdt, Benoît Depaire, Marlon Dumas, and Gert Janssenswillen. "Conformance Checking in Process Mining." Information Systems 102 (December 2021): 101851. http://dx.doi.org/10.1016/j.is.2021.101851.

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Rischawe, Richard, and Rüdiger Buck-Emden. "Process-Mining in der Assekuranz." HMD Praxis der Wirtschaftsinformatik 52, no. 3 (May 7, 2015): 433–43. http://dx.doi.org/10.1365/s40702-015-0137-1.

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van der Aalst, Wil M. P., and Schahram Dustdar. "Process Mining Put into Context." IEEE Internet Computing 16, no. 1 (January 2012): 82–86. http://dx.doi.org/10.1109/mic.2012.12.

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Dahlin, Sara, Henrik Eriksson, and Hendry Raharjo. "Process Mining for Quality Improvement." Quality Management in Health Care 28, no. 1 (2019): 8–14. http://dx.doi.org/10.1097/qmh.0000000000000197.

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Ingvaldsen, Jon Espen, and Jon Atle Gulla. "Model-Based Business Process Mining." Information Systems Management 23, no. 1 (December 2006): 19–31. http://dx.doi.org/10.1201/1078.10580530/45769.23.1.20061201/91769.3.

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Partington, Andrew, Moe Wynn, Suriadi Suriadi, Chun Ouyang, and Jonathan Karnon. "Process Mining for Clinical Processes." ACM Transactions on Management Information Systems 5, no. 4 (March 21, 2015): 1–18. http://dx.doi.org/10.1145/2629446.

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van der Aalst, W. M. P., and A. J. M. M. Weijters. "Process mining: a research agenda." Computers in Industry 53, no. 3 (April 2004): 231–44. http://dx.doi.org/10.1016/j.compind.2003.10.001.

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Rovani, Marcella, Fabrizio M. Maggi, Massimiliano de Leoni, and Wil M. P. van der Aalst. "Declarative process mining in healthcare." Expert Systems with Applications 42, no. 23 (December 2015): 9236–51. http://dx.doi.org/10.1016/j.eswa.2015.07.040.

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Mannhardt, Felix, Agnes Koschmider, Nathalie Baracaldo, Matthias Weidlich, and Judith Michael. "Privacy-preserving Process Mining: Differential." Informatik Spektrum 42, no. 5 (August 28, 2019): 349–51. http://dx.doi.org/10.1007/s00287-019-01207-9.

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Stocker, Thomas, Rafael Accorsi, and Tobias Rother. "Computergestützte Prozessauditierung mit Process Mining." HMD Praxis der Wirtschaftsinformatik 50, no. 4 (August 2013): 92–103. http://dx.doi.org/10.1007/bf03340838.

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Greco, Gianluigi, Antonella Guzzo, and Luigi Pontieri. "Mining taxonomies of process models." Data & Knowledge Engineering 67, no. 1 (October 2008): 74–102. http://dx.doi.org/10.1016/j.datak.2008.06.010.

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МАХМУДОВА, И. Н. "COMPLIANCE SYSTEM’S TOOLS: PROCESS MINING." Экономика и предпринимательство, no. 10(159) (December 4, 2023): 1219–23. http://dx.doi.org/10.34925/eip.2023.159.10.249.

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В статье исследуется проблема коррупционных рисков организации. Антикоррупционная политика - это часть системы экономической безопасности организации. Для её реализации целесообразно на постоянной основе организовывать функционирование комплаенс системы. В статье раскрывается суть комплаенс системы, основные направления её деятельности и новые инструменты анализа - процессная аналитика. Представлена структура антикоррупционной политики организации. Разработана карта антикоррупционных рисков. The article examines the problem of corruption risks of the organization. Anti-corruption policy is a part of the organization's economic security system. To implement it, it is advisable to organize the functioning of the compliance system on an ongoing basis. The article reveals the essence of the compliance system, the main directions of its activities and new analysis tools - process analytics. The structure of the organization's anti-corruption policy is presented. A map of anti-corruption risks has been developed.
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Li, Jiexun, Harry Jiannan Wang, Zhu Zhang, and J. Leon Zhao. "A policy-based process mining framework: mining business policy texts for discovering process models." Information Systems and e-Business Management 8, no. 2 (April 11, 2009): 169–88. http://dx.doi.org/10.1007/s10257-009-0112-x.

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Yogi, Supra, Angelina Prima Kurniati, and Ichwanul Muslim Karo Karo. "Process Mining on New Student Admission Process in Telkom University using Genetic Miner." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 5 (October 28, 2023): 1233–38. http://dx.doi.org/10.29207/resti.v7i5.5241.

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The selection process for new students at Telkom University, also known as SMB Telkom University has been running for years and already has its process flow. However, the existing process flow can be further improved to better reflect the actual field processes and become more accurate. Process mining can enhance this process flow by creating a new process flow based on event logs or previously executed processes. One of the algorithms in process mining is genetic process mining, where process mining is performed multiple times over several generations and genetic algorithms such as crossover and mutation are applied to generate a more accurate process model compared to other process mining algorithms such as heuristic and inductive mining. After conducting experiments, the best process model that was produced was at the 100th generation which has a fitness point of 0.755910819 and precision point of 0.742857143, after examining the parameters and the resulting Petri net or process flow that was produced it was concluded that the process model obtained from the application of Genetic Process Mining to SMB Telkom University is not very good because the resulting Petri net has several duplicate activities and appears to be non-linear. This could be due to several factors i.e., incompatible, or inaccurate data.
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Pika, Anastasiia, Moe T. Wynn, Stephanus Budiono, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, and Hajo A. Reijers. "Privacy-Preserving Process Mining in Healthcare." International Journal of Environmental Research and Public Health 17, no. 5 (March 2, 2020): 1612. http://dx.doi.org/10.3390/ijerph17051612.

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Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.
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48

Cho, Minsu, Minseok Song, Junhyun Park, Seok-Ran Yeom, Il-Jae Wang, and Byung-Kwan Choi. "Process Mining-Supported Emergency Room Process Performance Indicators." International Journal of Environmental Research and Public Health 17, no. 17 (August 28, 2020): 6290. http://dx.doi.org/10.3390/ijerph17176290.

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Emergency room processes are often exposed to the risk of unexpected factors, and process management based on performance measurements is required due to its connectivity to the quality of care. Regarding this, there have been several attempts to propose a method to analyze the emergency room processes. This paper proposes a framework for process performance indicators utilized in emergency rooms. Based on the devil’s quadrangle, i.e., time, cost, quality, and flexibility, the paper suggests multiple process performance indicators that can be analyzed using clinical event logs and verify them with a thorough discussion with clinical experts in the emergency department. A case study is conducted with the real-life clinical data collected from a tertiary hospital in Korea to validate the proposed method. The case study demonstrated that the proposed indicators are well applied using the clinical data, and the framework is capable of understanding emergency room processes’ performance.
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49

Farjana, Shahjadi Hisan, M. A. Parvez Mahmud, and Nazmul Huda. "Solar process heat integration in lead mining process." Case Studies in Thermal Engineering 22 (December 2020): 100768. http://dx.doi.org/10.1016/j.csite.2020.100768.

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50

M. Valle, Arthur, Eduardo A.P. Santos, and Eduardo R. Loures. "Applying process mining techniques in software process appraisals." Information and Software Technology 87 (July 2017): 19–31. http://dx.doi.org/10.1016/j.infsof.2017.01.004.

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