Relevant bibliographies by topics / Process mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Process mining'

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Published: 4 June 2021
Last updated: 21 September 2024

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Journal articles on the topic "Process mining"

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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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Dissertations / Theses on the topic "Process mining"

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van, der Aalst Wil M. P., Arya Adriansyah, Alves de Medeiros Ana Karla, Franco Arcieri, Thomas Baier, Tobias Blickle, Jagadeesh Chandra Bose R. P, et al. "Process Mining Manifesto." Springer, 2011. http://dx.doi.org/10.1007/978-3-642-28108-2_19.

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Process mining techniques are able to extract knowledge from event logs commonly available in today's information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.
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Khodabandelou, Ghazaleh. "Mining Intentional Process Models." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2014. http://tel.archives-ouvertes.fr/tel-01010756.

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Jusqu'à présent, les techniques de fouille de processus ont modélisé les processus en termes des séquences de tâches qui se produisent lors de l'exécution d'un processus. Cependant, les recherches en modélisation du processus et de guidance ont montrée que de nombreux problèmes, tels que le manque de flexibilité ou d'adaptation, sont résolus plus efficacement lorsque les intentions sont explicitement spécifiées. Cette thèse présente une nouvelle approche de fouille de processus, appelée Map Miner méthode (MMM). Cette méthode est conçue pour automatiser la construction d'un modèle de processus intentionnel à partir des traces d'activités des utilisateurs. MMM utilise les modèles de Markov cachés pour modéliser la relation entre les activités des utilisateurs et leurs stratégies (i.e., les différentes façons d'atteindre des intentions). La méthode comprend également deux algorithmes spécifiquement développés pour déterminer les intentions des utilisateurs et construire le modèle de processus intentionnel de la Carte. MMM peut construire le modèle de processus de la Carte avec différents niveaux de précision (pseudo-Carte et le modèle du processus de la carte) par rapport au formalisme du métamodèle de Map. L'ensemble de la méthode proposée a été appliqué et validé sur des ensembles de données pratiques, dans une expérience à grande échelle, sur les traces d'événements des développeurs de Eclipse UDC.
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Remberg, Julia. "Grundlagen des Process Mining : [Studienarbeit] /." [München] : Grin-Vel, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017676071&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Nguyen, Hoang H. "Stage-aware business process mining." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130602/9/Hoang%20Nguyen%20Thesis.pdf.

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Process mining enables the analysis of event logs to gain actionable insights into an organisation’s operations. However, state-of-the-art process mining techniques do not exploit the natural decomposition characteristics of business processes. “Process stages” are a generic type of business process decomposition prevalent in multiple domains, e.g. the stages of loan processing, the support levels in IT helpdesk, or the clinical stages in patient treatment. This study contributes a novel approach to process mining based on process stages. The approach is grounded on four techniques that allow the mining of process stages, the automated discovery of process models, the mining of process performance and the multi-perspective comparison of process variants. The approach has been implemented in an open-source toolset and evaluated with real-life datasets from different domains.
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Baier, Thomas, Jan Mendling, and Mathias Weske. "Bridging abstraction layers in process mining." Elsevier, 2014. http://dx.doi.org/10.1016/j.is.2014.04.004.

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While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction try to abstract from the events in an automated way that does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach that aims to abstract an event log to the same abstraction level that is needed by the business. We use domain knowledge extracted from existing process documentation to semi-automatically match events and activities. Our abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in two case studies with a German IT outsourcing company.
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Pika, Anastasiia. "Mining process risks and resource profiles." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/86079/1/Anastasiia_Pika_Thesis.pdf.

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This research contributes novel techniques for identifying and evaluating business process risks and analysing human resource behaviour. The developed techniques use predefined indicators to identify process risks in individual process instances, evaluate overall process risk, predict process outcomes and analyse human resource behaviour based on the analysis of information about process executions recorded in event logs by information systems. The results of this research can help managers to more accurately evaluate the risk exposure of their business processes, to more objectively evaluate the performance of their employees, and to identify opportunities for improvement of resource and process performance.
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Gerke, Kerstin. "Continual process improvement based on reference models and process mining." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2011. http://dx.doi.org/10.18452/16353.

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Geschäftsprozesse stellen ein wichtiges Gut eines Unternehmens dar. Für den Unternehmenserfolg sind nicht einmalig optimal gestaltete Prozesse entscheidend, sondern die Fähigkeit, schnell auf neue Entwicklungen reagieren und die betroffenen Prozesse flexibel anpassen zu können. In vielen Unternehmen ist eine aktuelle Beschreibung ihrer Prozesse als wesentliche Voraussetzung für die Prozessverbesserung jedoch nicht oder nur unzureichend gegeben. Nicht selten wird ein erstelltes Prozessmodell nicht weiterverwendet, so dass es nach kurzer Zeit von der betrieblichen Realität abweicht. Diese fehlende Übereinstimmung kann durch die Nutzung von Prozess-Mining-Technologien verhindert werden, indem das in den Informationssystemen implizit vorhandene Prozesswissen automatisiert extrahiert und in Form von Prozessmodellen abgebildet wird. Ein weiteres wichtiges Element für die effiziente Gestaltung und Steuerung von Prozessen bilden Referenzmodelle, wie z. B. ITIL und CobiT. Die Prozessverbesserung durchläuft in der Regel mehrere Analyse-, Design-, Implementierungs- , Ausführungs-, Monitoring-, und Evaluierungsschritte. Die Arbeit stellt eine Methodik vor, die die Identifizierung und Lösung der auftretenden Aufgaben unterstützt und erleichtert. Eine empirische Untersuchung zeigt die Herausforderungen und die Potenziale für den erfolgreichen Einsatz von Process-Mining-Techniken. Auf der Basis der Resultate dieser Untersuchung wurden spezielle Aspekte der Datenaufbereitung für Process-Mining-Algorithmen detailliert betrachtet. Der Fokus liegt dabei auf der Bereitstellung von Enterprise- und RFID-Daten. Weiterhin beleuchtet die Arbeit die Wichtigkeit, die Referenzprozessausführung zu überprüfen, um deren Einhaltung in Bezug auf neue oder geänderte Prozesse zu sichern. Die Methodik wurde anhand einer Reihe von Praxisbeispielen erprobt. Die Ergebnisse unterstreichen ihre generelle unternehmensübergreifende Anwendbarkeit für die effiziente kontinuierliche Prozessverbesserung.
The dissertation at hand takes as its subject business processes. Naturally they are subject to continual improvement and are a major asset of any given organization. An optimally-designed process, having once proven itself, must be flexible, as new developments demand swift adaptations. However, many organizations do not adequately describe these processes, though doing so is a prerequisite for their improvement. Very often the process model created during an information system’s implementation either is not used in the first place or is not maintained, resulting in an obvious lack of correspondence between the model and operational reality. Process mining techniques prevent this. They extract the process knowledge inherent in an information system and visualize it in the form of process models. Indeed, continual process improvement depends greatly on this modeling approach, and reference models, such as ITIL and CobiT, are entirely suitable and powerful means for dealing with the efficient design and control of processes. Process improvement typically consists of a number of analysis, design, implementation, execution, monitoring, and evaluation activities. This dissertation proposes a methodology that supports and facilitates them. An empirical analysis both revealed the challenges and the potential benefits of these processes mining techniques’ successful. This in turn led to the detailed consideration of specific aspects of the data preparation for process mining algorithms. Here the focus is on the provision of enterprise data and RFID events. This dissertation as well examines the importance of analyzing the execution of reference processes to ensure compliance with modified or entirely new business processes. The methodology involved a number of cases’ practical trials; the results demonstrate its power and universality. This new approach ushers in an enhanced continual inter-departmental and inter-organizational improvement process.
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Muñoz, Gama Jorge. "Conformance checking and diagnosis in process mining." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/284964.

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In the last decades, the capability of information systems to generate and record overwhelming amounts of event data has experimented an exponential growth in several domains, and in particular in industrial scenarios. Devices connected to the internet (internet of things), social interaction, mobile computing, and cloud computing provide new sources of event data and this trend will continue in the next decades. The omnipresence of large amounts of event data stored in logs is an important enabler for process mining, a novel discipline for addressing challenges related to business process management, process modeling, and business intelligence. Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The capability of these models to represent the reality determines the quality of the results obtained from them, conditioning its usefulness. Conformance checking is the aim of this thesis, where modeled and observed behavior are analyzed to determine if a model defines a faithful representation of the behavior observed a the log. Most of the efforts in conformance checking have focused on measuring and ensuring that models capture all the behavior in the log, i.e., fitness. Other properties, such as ensuring a precise model (not including unnecessary behavior) have been disregarded. The first part of the thesis focuses on analyzing and measuring the precision dimension of conformance, where models describing precisely the reality are preferred to overly general models. The thesis includes a novel technique based on detecting escaping arcs, i.e., points where the modeled behavior deviates from the one reflected in log. The detected escaping arcs are used to determine, in terms of a metric, the precision between log and model, and to locate possible actuation points in order to achieve a more precise model. The thesis also presents a confidence interval on the provided precision metric, and a multi-factor measure to assess the severity of the detected imprecisions. Checking conformance can be time consuming for real-life scenarios, and understanding the reasons behind the conformance mismatches can be an effort-demanding task. The second part of the thesis changes the focus from the precision dimension to the fitness dimension, and proposes the use of decomposed techniques in order to aid in checking and diagnosing fitness. The proposed approach is based on decomposing the model into single entry single exit components. The resulting fragments represent subprocesses within the main process with a simple interface with the rest of the model. Fitness checking per component provides well-localized conformance information, aiding on the diagnosis of the causes behind the problems. Moreover, the relations between components can be exploded to improve the diagnosis capabilities of the analysis, identifying areas with a high degree of mismatches, or providing a hierarchy for a zoom-in zoom-out analysis. Finally, the thesis proposed two main applications of the decomposed approach. First, the theory proposed is extended to incorporate data information for fitness checking in a decomposed manner. Second, a real-time event-based framework is presented for monitoring fitness.
En las últimas décadas, la capacidad de los sistemas de información para generar y almacenar datos de eventos ha experimentado un crecimiento exponencial, especialmente en contextos como el industrial. Dispositivos conectados permanentemente a Internet (Internet of things), redes sociales, teléfonos inteligentes, y la computación en la nube proporcionan nuevas fuentes de datos, una tendencia que continuará en los siguientes años. La omnipresencia de grandes volúmenes de datos de eventos almacenados en logs abre la puerta al Process Mining (Minería de Procesos), una nueva disciplina a caballo entre las técnicas de gestión de procesos de negocio, el modelado de procesos, y la inteligencia de negocio. Las técnicas de minería de procesos pueden usarse para descubrir, analizar, y mejorar procesos reales, a base de extraer modelos a partir del comportamiento observado. La capacidad de estos modelos para representar la realidad determina la calidad de los resultados que se obtengan, condicionando su efectividad. El Conformance Checking (Verificación de Conformidad), objetivo final de esta tesis, permite analizar los comportamientos observados y modelados, y determinar si el modelo es una fiel representación de la realidad. La mayoría de los esfuerzos en Conformance Checking se han centrado en medir y asegurar que los modelos fueran capaces de capturar todo el comportamiento observado, también llamado "fitness". Otras propiedades, tales como asegurar la "precisión" de los modelos (no modelar comportamiento innecesario) han sido relegados a un segundo plano. La primera parte de esta tesis se centra en analizar la precisión, donde modelos describiendo la realidad con precisión son preferidos a modelos demasiado genéricos. La tesis presenta una nueva técnica basada en detectar "arcos de escape", i.e. puntos donde el comportamiento modelado se desvía del comportamiento reflejado en el log. Estos arcos de escape son usados para determinar, en forma de métrica, el nivel de precisión entre un log y un modelo, y para localizar posibles puntos de mejora. La tesis también presenta un intervalo de confianza sobre la métrica, así como una métrica multi-factorial para medir la severidad de las imprecisiones detectadas. Conformance Checking puede ser una operación costosa para escenarios reales, y entender las razones que causan los problemas requiere esfuerzo. La segunda parte de la tesis cambia el foco (de precisión a fitness), y propone el uso de técnicas de descomposición para ayudar en la verificación de fitness. Las técnicas propuestas se basan en descomponer el modelo en componentes con una sola entrada y una sola salida, llamados SESEs. Estos componentes representan subprocesos dentro del proceso principal. Verificar el fitness a nivel de subproceso proporciona una información detallada de dónde están los problemas, ayudando en su diagnóstico. Además, las relaciones entre subprocesos pueden ser explotadas para mejorar las capacidades de diagnóstico e identificar qué áreas concentran la mayor densidad de problemas. Finalmente, la tesis propone dos aplicaciones directas de las técnicas de descomposición: 1) la teoría es extendida para incluir información de datos a la verificación de fitness, y 2) el uso de sistemas descompuestos en tiempo real para monitorizar fitness
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Selig, Henny. "Continuous Event Log Extraction for Process Mining." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210710.

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Process mining is the application of data science technologies on transactional business data to identify or monitor processes within an organization. The analyzed data often originates from process-unaware enterprise software, e.g. Enterprise Resource Planning (ERP) systems. The differences in data management between ERP and process mining systems result in a large fraction of ambiguous cases, affected by convergence and divergence. The consequence is a chasm between the process as interpreted by process mining, and the process as executed in the ERP system. In this thesis, a purchasing process of an SAP ERP system is used to demonstrate, how ERP data can be extracted and transformed into a process mining event log that expresses ambiguous cases as accurately as possible. As the content and structure of the event log already define the scope (i.e. which process) and granularity (i.e. activity types), the process mining results depend on the event log quality. The results of this thesis show how the consideration of case attributes, the notion of a case and the granularity of events can be used to manage the event log quality. The proposed solution supports continuous event extraction from the ERP system.
Process mining är användningen av datavetenskaplig teknik för transaktionsdata, för att identifiera eller övervaka processer inom en organisation. Analyserade data härstammar ofta från processomedvetna företagsprogramvaror, såsom SAP-system, vilka är centrerade kring affärsdokumentation. Skillnaderna i data management mellan Enterprise Resource Planning (ERP)och process mining-system resulterar i en stor andel tvetydiga fall, vilka påverkas av konvergens och divergens. Detta resulterar i ett gap mellan processen som tolkas av process mining och processen som exekveras i ERP-systemet. I denna uppsats används en inköpsprocess för ett SAP ERP-system för att visa hur ERP-data kan extraheras och omvandlas till en process mining-orienterad händelselogg som uttrycker tvetydiga fall så precist som möjligt. Eftersom innehållet och strukturen hos händelseloggen redan definierar omfattningen (vilken process) och granularitet (aktivitetstyperna), så beror resultatet av process mining på kvalitén av händelseloggen. Resultaten av denna uppsats visar hur definitioner av typfall och händelsens granularitet kan användas för att förbättra kvalitén. Den beskrivna lösningen stöder kontinuerlig händelseloggsextraktion från ERPsystemet.
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Munoz-Gama, Jorge. "Conformance checking and diagnosis in process mining." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/284964.

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In the last decades, the capability of information systems to generate and record overwhelming amounts of event data has experimented an exponential growth in several domains, and in particular in industrial scenarios. Devices connected to the internet (internet of things), social interaction, mobile computing, and cloud computing provide new sources of event data and this trend will continue in the next decades. The omnipresence of large amounts of event data stored in logs is an important enabler for process mining, a novel discipline for addressing challenges related to business process management, process modeling, and business intelligence. Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The capability of these models to represent the reality determines the quality of the results obtained from them, conditioning its usefulness. Conformance checking is the aim of this thesis, where modeled and observed behavior are analyzed to determine if a model defines a faithful representation of the behavior observed a the log. Most of the efforts in conformance checking have focused on measuring and ensuring that models capture all the behavior in the log, i.e., fitness. Other properties, such as ensuring a precise model (not including unnecessary behavior) have been disregarded. The first part of the thesis focuses on analyzing and measuring the precision dimension of conformance, where models describing precisely the reality are preferred to overly general models. The thesis includes a novel technique based on detecting escaping arcs, i.e., points where the modeled behavior deviates from the one reflected in log. The detected escaping arcs are used to determine, in terms of a metric, the precision between log and model, and to locate possible actuation points in order to achieve a more precise model. The thesis also presents a confidence interval on the provided precision metric, and a multi-factor measure to assess the severity of the detected imprecisions. Checking conformance can be time consuming for real-life scenarios, and understanding the reasons behind the conformance mismatches can be an effort-demanding task. The second part of the thesis changes the focus from the precision dimension to the fitness dimension, and proposes the use of decomposed techniques in order to aid in checking and diagnosing fitness. The proposed approach is based on decomposing the model into single entry single exit components. The resulting fragments represent subprocesses within the main process with a simple interface with the rest of the model. Fitness checking per component provides well-localized conformance information, aiding on the diagnosis of the causes behind the problems. Moreover, the relations between components can be exploded to improve the diagnosis capabilities of the analysis, identifying areas with a high degree of mismatches, or providing a hierarchy for a zoom-in zoom-out analysis. Finally, the thesis proposed two main applications of the decomposed approach. First, the theory proposed is extended to incorporate data information for fitness checking in a decomposed manner. Second, a real-time event-based framework is presented for monitoring fitness.
En las últimas décadas, la capacidad de los sistemas de información para generar y almacenar datos de eventos ha experimentado un crecimiento exponencial, especialmente en contextos como el industrial. Dispositivos conectados permanentemente a Internet (Internet of things), redes sociales, teléfonos inteligentes, y la computación en la nube proporcionan nuevas fuentes de datos, una tendencia que continuará en los siguientes años. La omnipresencia de grandes volúmenes de datos de eventos almacenados en logs abre la puerta al Process Mining (Minería de Procesos), una nueva disciplina a caballo entre las técnicas de gestión de procesos de negocio, el modelado de procesos, y la inteligencia de negocio. Las técnicas de minería de procesos pueden usarse para descubrir, analizar, y mejorar procesos reales, a base de extraer modelos a partir del comportamiento observado. La capacidad de estos modelos para representar la realidad determina la calidad de los resultados que se obtengan, condicionando su efectividad. El Conformance Checking (Verificación de Conformidad), objetivo final de esta tesis, permite analizar los comportamientos observados y modelados, y determinar si el modelo es una fiel representación de la realidad. La mayoría de los esfuerzos en Conformance Checking se han centrado en medir y asegurar que los modelos fueran capaces de capturar todo el comportamiento observado, también llamado "fitness". Otras propiedades, tales como asegurar la "precisión" de los modelos (no modelar comportamiento innecesario) han sido relegados a un segundo plano. La primera parte de esta tesis se centra en analizar la precisión, donde modelos describiendo la realidad con precisión son preferidos a modelos demasiado genéricos. La tesis presenta una nueva técnica basada en detectar "arcos de escape", i.e. puntos donde el comportamiento modelado se desvía del comportamiento reflejado en el log. Estos arcos de escape son usados para determinar, en forma de métrica, el nivel de precisión entre un log y un modelo, y para localizar posibles puntos de mejora. La tesis también presenta un intervalo de confianza sobre la métrica, así como una métrica multi-factorial para medir la severidad de las imprecisiones detectadas. Conformance Checking puede ser una operación costosa para escenarios reales, y entender las razones que causan los problemas requiere esfuerzo. La segunda parte de la tesis cambia el foco (de precisión a fitness), y propone el uso de técnicas de descomposición para ayudar en la verificación de fitness. Las técnicas propuestas se basan en descomponer el modelo en componentes con una sola entrada y una sola salida, llamados SESEs. Estos componentes representan subprocesos dentro del proceso principal. Verificar el fitness a nivel de subproceso proporciona una información detallada de dónde están los problemas, ayudando en su diagnóstico. Además, las relaciones entre subprocesos pueden ser explotadas para mejorar las capacidades de diagnóstico e identificar qué áreas concentran la mayor densidad de problemas. Finalmente, la tesis propone dos aplicaciones directas de las técnicas de descomposición: 1) la teoría es extendida para incluir información de datos a la verificación de fitness, y 2) el uso de sistemas descompuestos en tiempo real para monitorizar fitness
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Books on the topic "Process mining"

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van der Aalst, Wil. Process Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49851-4.

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van der Aalst, Wil M. P. Process Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19345-3.

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Peters, Ralf, and Markus Nauroth. Process-Mining. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24170-4.

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Leemans, Sander, and Henrik Leopold, eds. Process Mining Workshops. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72693-5.

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Munoz-Gama, Jorge, and Xixi Lu, eds. Process Mining Workshops. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3.

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van der Aalst, Wil M. P., and Josep Carmona, eds. Process Mining Handbook. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08848-3.

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De Smedt, Johannes, and Pnina Soffer, eds. Process Mining Workshops. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56107-8.

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Montali, Marco, Arik Senderovich, and Matthias Weidlich, eds. Process Mining Workshops. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0.

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Mans, Ronny S., Wil M. P. van der Aalst, and Rob J. B. Vanwersch. Process Mining in Healthcare. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16071-9.

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Reinkemeyer, Lars, ed. Process Mining in Action. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40172-6.

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Book chapters on the topic "Process mining"

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van der Aalst, Wil. "Data Mining." In Process Mining, 89–121. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49851-4_4.

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van der Aalst, Wil M. P. "Data Mining." In Process Mining, 59–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19345-3_3.

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van der Aalst, Wil M. P., and A. J. M. M. Ton Weijters. "Process Mining." In Process-Aware Information Systems, 235–55. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471741442.ch10.

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Taulli, Tom. "Process Mining." In The Robotic Process Automation Handbook, 273–92. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5729-6_12.

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van der Aalst, W. M. P. "Process Mining." In Encyclopedia of Database Systems, 1–3. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_1477-2.

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Burattin, Andrea. "Process Mining." In Process Mining Techniques in Business Environments, 33–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17482-2_5.

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van der Aalst, W. M. P. "Process Mining." In Encyclopedia of Database Systems, 2171–73. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1477.

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Scheer, August-Wilhelm. "Process Mining." In Unternehmung 4.0, 85–102. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-27694-2_5.

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Mans, Ronny S., Wil M. P. van der Aalst, and Rob J. B. Vanwersch. "Process Mining." In Process Mining in Healthcare, 17–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16071-9_3.

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Zhang, Limao, Yue Pan, Xianguo Wu, and Mirosław J. Skibniewski. "Process Mining." In Lecture Notes in Civil Engineering, 147–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2842-9_7.

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Conference papers on the topic "Process mining"

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Schweidtmann, Artur M. "Mining Chemical Process Information from Literature for Generative Process Design: A Perspective." In Foundations of Computer-Aided Process Design, 84–91. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.184704.

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Artificial intelligence (AI) and particularly generative AI led to recent breakthroughs, e.g., in generating text and images. There is also a potential of these technologies in chemical engineering, but the lack of structured big domain-relevant data hinders advancements. I envision an open Chemical Engineering Knowledge Graph (ChemEngKG) that provides big open and linked chemical process information. In this article, I present the concept of �flowsheet mining� as the first step towards the ChemEngKG. Flowsheet mining extracts process information from flowsheets and process descriptions found in scientific literature and patents. The proposed technology requires the integration of data mining, computer vision, natural language processing, and semantic web technologies. I present the concept of flowsheet mining, discuss previous literature, and show future potentials. I believe the availability of big data will enable breakthroughs in process design through artificial intelligence.
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Elbert, Nico, and Christoph M. Flath. "Process Mining for Game Analytics." In 2024 IEEE Conference on Games (CoG), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/cog60054.2024.10645544.

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Pegoraro, Marco, and Wil M. P. van der Aalst. "Mining Uncertain Event Data in Process Mining." In 2019 International Conference on Process Mining (ICPM). IEEE, 2019. http://dx.doi.org/10.1109/icpm.2019.00023.

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Vogelgesang, Thomas, and H. Jürgen Appelrath. "Multidimensional process mining." In the Joint EDBT/ICDT 2013 Workshops. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2457317.2457321.

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Pena, Marcos Rivas, and Sussy Bayona-Ore. "Process Mining and Automatic Process Discovery." In 2018 7th International Conference On Software Process Improvement (CIMPS). IEEE, 2018. http://dx.doi.org/10.1109/cimps.2018.8625621.

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Erdogan, Tugba, and Ayca Tarhan. "Process Mining for Healthcare Process Analytics." In 2016 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (IWSM-MENSURA). IEEE, 2016. http://dx.doi.org/10.1109/iwsm-mensura.2016.027.

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de Jong, Robin, Sam Leewis, and Matthijs Berkhout. "Decision Mining versus Process Mining: a Comparison of Mining Methods." In ICSEB 2021: 2021 5th International Conference on Software and e-Business. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3507485.3507490.

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Bratosin, Carmen, Natalia Sidorova, and Wil van der Aalst. "Distributed genetic process mining." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586250.

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Blum, Fabian Rojas. "Mining software process lines." In ICSE '16: 38th International Conference on Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2889160.2889267.

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Delcoucq, Landelin, Fabian Lecron, Philippe Fortemps, and Wil M. P. van der Aalst. "Resource-centric process mining." In SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3341105.3373864.

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Reports on the topic "Process mining"

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Ступнік, М. І., В. С. Моркун, and З. П. Бакум. Information and Communication Technologies in the Process of Mining Engineer Training. Криворізький державний педагогічний університет, 2013. http://dx.doi.org/10.31812/0564/405.

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Based on scientific analysis the authors of the article argued the necessity of solving priority tasks – the development of new educational technologies aimed at supporting the training of engineers in terms of the mining engineering as high-tech industry. The features of mining computer technologies are determined. There was worked out the project of the adaptive system of a mining engineer individual training "Electronic manual" aimed at the development of future professionals. The essence of individual preparation of future mining engineer ICT is defined. It is proved that the efficiency of the designing and planning of mining operations through the introduction of ICT at present is the real way to influence the quality of mining products that will promote individual learning orientation. For the first time pedagogical foundations for introducing adaptive training of mining engineers are clarified.
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Scully, Brandan. Tidal analysis and arrival process mining using Automatic Identification System (AIS) data. Coastal and Hydraulics Laboratory (U.S.), February 2017. http://dx.doi.org/10.21079/11681/21465.

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Бакум, З. П., and В. В. Ткачук. Mining Engineers Training in Context of Innovative System of Ukraine. Криворізький державний педагогічний університет, 2014. http://dx.doi.org/10.31812/0564/425.

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The paper clarifies the process of a mining engineer training in terms of the innovation system of Ukraine. The authors analyze a number of normative documents concerning innovative activity in Ukraine in general and mining business in particular. In the process of implementation of innovations into mining industrial complex urgent problems are defined. The methodology of information and communication technologies (electronic, distance and mobile studies) usage in engineers training within the conditions of university education is offered. It is marked that the worked out methodology finds its practical introduction: e-learning involves creation of the portal "Electronic mentor"; distance learning is presented in the study of professional disciplines as an example of the course "Сomputer Technologies in Mining"; mobile learning is considered as an example of discipline "Computer Science and Engineering".
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Priester, Michael, Malaika Masson, and Martin Walter. Incentivizing Clean Technology in the Mining Sector in Latin America and the Caribbean: The Role of Public Mining Institutions. Inter-American Development Bank, December 2013. http://dx.doi.org/10.18235/0009148.

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How can LAC governments promote the use of clean and green technology in the mining sector and what are the supporting instruments, regulations, infrastructure and institutional aspects that are needed to reinforce this role within public supervisory mining agencies? This technical note explores opportunities for incentivizing cleaner technologies in mining in Latin America and the Caribbean (LAC) region. It focuses on two aspects: key conceptual notions related to clean technologies/process in mining and the practical efforts required by governments to monitor and regulate their use in LAC. It showcases the case of Bolivia, Guyana, and Peru, and identifies specific avenues for the improved capture of economic value from mining, while minimizing negative environmental and social impacts.
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Morkun, Volodymyr, Sergey Semerikov, Svitlana Hryshchenko, Snizhana Zelinska, and Serhii Zelinskyi. Environmental Competence of the Future Mining Engineer in the Process of the Training. Medwell Publishing, 2017. http://dx.doi.org/10.31812/0564/1523.

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A holistic solution to the problem of formation of ecological competence of the future engineer requires the definition of its content, structure, place in the system of professional competences, levels of forming and criteria of measurement the rationale for the select on and development of a technique of use of information, communication and learning technologies that promote formation of ecological competence. The study is of interest to environmental competence of future mining engineer as personal education, characterized by acquired in the process of professional preparation professionally oriented environmental knowledge (cognitive criterion), learned the ways of securing environmentally safe mining works (praxiological criterion) in the interests of sustainable development (axiological criterion) and is formed by the qualities of socially responsible environmental behavior (social-behavioral criterion) and consists of the following components: understanding and perception of ethical norms of behaviour towards other people and towards nature (the principles of bioethics); ecological literacy; possession of basic information on the ecology necessary for usage in professional activity the ability to use scientific laws and methods in evaluating the environment to participate in environmental works to cany out ecological analysis of activities in the area industrial activities to develop action plans for the reduction of the anthropogenic impact on the environment; ability to ensure environmentally balanced activities, possession of methods of rational and integrated development georesource potential of the subsoil.
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Morkun, Volodymyr S., Сергій Олексійович Семеріков, Svitlana M. Hryshchenko, and Kateryna I. Slovak. System of competencies for mining engineers. Видавництво “CSITA”, 2016. http://dx.doi.org/10.31812/0564/719.

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Topicality of the material, highlighted in this article is stipulated by the need to ensure effectiveness of educational process while preparing mining engineers. System of competencies for future mining engineers, taken as basis for high school sectoral standard for Mining 6.050301 update is theoretically substantiated and developed. Sources of state-of-the-art foreign educational system and technologies as well as scientific research results of local teachers have been analyzed, enabling development of new sectoral standard. Switching to new high school competencies-based sectoral standards is the necessary step in high education reforming in Ukraine, while the application of competencies-based approach to high school sectoral standards development facilitates tuning of education towards labour market’s requirements and demands, further development of educational techniques and educational system as a whole. Objective of the article: to project system of competencies and to define components of environmental competencies for mining engineers. Methods: – theoretical: analysis, generalization, systematization of legislative framework, educational standards, Internet - sources in order to distinguish theoretical basis of research, develop system of competencies for future mining engineers. – Empirical – improvement of system of competencies for future mining engineers. Scientific novelty is represented with structured system, consisting of 49 competencies, comprising the core of new sectoral standard for mining engineers preparation; Practical importance of the outcomes is related to developments: separate constituents of high school draft sectoral standard for Mining engineers bachelors’ preparation 6.050301 Mining (system of social & personal, general scientific, tool-based, general professional and special professional competencies. Research outcomes can be used while developing educational qualification profile and training program for Mining bachelors 6.050301 education field, in course of geoinformational technologies review by ecology, land survey and geography bachelors.
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Pande, Rohini, and Anant Sudarshan. Harnessing transparency initiatives to improve India’s environmental clearance process for the mineral mining sector. International Initiative for Impact Evaluation (3ie), March 2019. http://dx.doi.org/10.23846/tw8ie92.

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Morkun, Vladimir S., Serhiy O. Semerikov, Nataliya V. Morkun, Svitlana M. Hryshchenko, and Arnold E. Kiv. Defining the Structure of Environmental Competence of Future Mining Engineers: ICT Approach. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2650.

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The object is to the reasonable selection of the ICT tools for formation of ecological competence. Pressing task is constructive and research approach to preparation of future engineers to performance of professional duties in order to make them capable to develop engineering projects independently and exercise control competently. Subject of research: the theoretical justification of competence system of future mining engineers. Methods: source analysis on the problem of ecological competence formation. Results: defining the structure of environmental competence of future mining engineers. Conclusion: the relevance of the material covered in the article, due to the need to ensure the effectiveness of the educational process in the preparation of the future mining engineers.
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Chamanara, Sanaz, and Kaveh Madani. The Hidden Environmental Cost of Cryptocurrency: How Bitcoin Mining Impacts Climate, Water and Land. United Nations University Institute for Water, Environment and Health (UNU INWEH), October 2023. http://dx.doi.org/10.53328/inr23asc02.

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Based on a multi-attribute assessment of the environmental impacts and challenges associated with global Bitcoin (BTC) mining activities around the globe, we call for urgent action by the scientific, policy, and advocacy communities. The worldwide BTC mining network consumed 173.42 TWh of electricity during the 2020–2021 period, bigger than the electricity consumption of most nations. The mining process emitted over 85.89 Mt of CO2eq in the same timeframe, equivalent to the emission caused by burning 84 billion pounds of coal or running 190 natural gas-fired power plants. The environmental footprint of BTC mining is not limited to greenhouse gas emissions. In 2020–2021, the global water footprint of BTC mining was about 1.65 km 3, more than the domestic water use of 300 million people in rural Sub-Saharan Africa. The land footprint of the global BTC mining network during this period was more than 1,870 square kilometers, 1.4 times the area of Los Angeles. These striking numbers highlight the heavy reliance of the BTC network on fossil fuels and natural resource-intensive energy sources, resulting in major but unmonitored and unregulated environmental footprints. To mitigate the environmental costs of BTC mining, immediate policy interventions, technological advancements, and scientific research are crucial. Proposed measures include enhanced transparency, economic and regulatory tools, developing energy-efficient alternative coins, and the adoption of greener blockchain validation protocols.
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Klasky, Hilda, and Ozgur Ozmen. Process Mining in Healthcare - A Case Study for the Corporate Data Warehouse of the Veterans Affairs Office. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1649520.

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