Dissertations / Theses: 'Business Process Mining'

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Ostovar, Alireza. "Business process drift: Detection and characterization." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127157/1/Alireza_Ostovar_Thesis.pdf.

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This research contributes a set of techniques for the early detection and characterization of process drifts, i.e. statistically significant changes in the behavior of business operations, as recorded in transactional data. Early detection and subsequent characterization of process drifts allows organizations to take prompt remedial actions and avoid potential repercussions resulting from unplanned changes in the behavior of their operations.

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Yongsiriwit, Karn. "Modeling and mining business process variants in cloud environments." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL002/document.

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De plus en plus les organisations adoptent les systèmes d'informations sensibles aux processus basés sur Cloud en tant qu'un environnement pour gérer et exécuter des processus dans le Cloud dans l'objectif de partager et de déployer leurs applications de manière optimale. Cela est particulièrement vrai pour les grandes organisations ayant des succursales opérant dans des différentes régions avec des processus considérablement similaires. Telles organisations doivent soutenir de nombreuses variantes du même processus en raison de la culture locale de leurs succursales, de leurs règlements, etc. Cependant, le développement d'une nouvelle variante de processus à partir de zéro est sujet à l'erreur et peut prendre beaucoup du temps. Motivés par le paradigme "la conception par la réutilisation", les succursales peuvent collaborer pour développer de nouvelles variantes de processus en apprenant de leurs processus similaires. Ces processus sont souvent hétérogènes, ce qui empêche une interopérabilité facile et dynamique entre les différentes succursales. Une variante de processus est un ajustement d'un modèle de processus afin de s'adapter d'une façon flexible aux besoins spécifiques. De nombreuses recherches dans les universités et les industries visent à faciliter la conception des variantes de processus. Plusieurs approches ont été développées pour aider les concepteurs de processus en recherchant des modèles de processus métier similaires ou en utilisant des modèles de référence. Cependant, ces approches sont lourdes, longues et sujettes à des erreurs. De même, telles approches recommandent des modèles de processus pas pratiques pour les concepteurs de processus qui ont besoin d'ajuster une partie spécifique d'un modèle de processus. En fait, les concepteurs de processus peuvent mieux développer des variantes de processus ayant une approche qui recommande un ensemble bien défini d'activités à partir d'un modèle de processus défini comme un fragment de processus. Les grandes organisations multi-sites exécutent les variantes de processus BP dans l'environnement Cloud pour optimiser le déploiement et partager les ressources communes. Cependant, ces ressources Cloud peuvent être décrites en utilisant des différents standards de description des ressources Cloud ce qui empêche l'interopérabilité entre les différentes succursales. Dans cette thèse, nous abordons les limites citées ci-dessus en proposant une approche basée sur les ontologies pour peupler sémantiquement une base de connaissance commune de processus et de ressources Cloud, ce qui permet une interopérabilité entre les succursales de l'organisation. Nous construisons notre base de connaissance en étendant les ontologies existantes. Ensuite, nous proposons une approche pour exploiter cette base de connaissances afin de supporter le développement des variantes BP. De plus, nous adoptons un algorithme génétique pour allouer d'une manière optimale les ressources Cloud aux BPs. Pour valider notre approche, nous développons deux preuves de concepts et effectuons des expériences sur des ensembles de données réels. Les résultats expérimentaux montrent que notre approche est réalisable et précise dans des cas d'utilisation réels More and more organizations are adopting cloud-based Process-Aware Information Systems (PAIS) to manage and execute processes in the cloud as an environment to optimally share and deploy their applications. This is especially true for large organizations having branches operating in different regions with a considerable amount of similar processes. Such organizations need to support many variants of the same process due to their branches' local culture, regulations, etc. However, developing new process variant from scratch is error-prone and time consuming. Motivated by the "Design by Reuse" paradigm, branches may collaborate to develop new process variants by learning from their similar processes. These processes are often heterogeneous which prevents an easy and dynamic interoperability between different branches. A process variant is an adjustment of a process model in order to flexibly adapt to specific needs. Many researches in both academics and industry are aiming to facilitate the design of process variants. Several approaches have been developed to assist process designers by searching for similar business process models or using reference models. However, these approaches are cumbersome, time-consuming and error-prone. Likewise, such approaches recommend entire process models which are not handy for process designers who need to adjust a specific part of a process model. In fact, process designers can better develop process variants having an approach that recommends a well-selected set of activities from a process model, referred to as process fragment. Large organizations with multiple branches execute BP variants in the cloud as environment to optimally deploy and share common resources. However, these cloud resources may be described using different cloud resources description standards which prevent the interoperability between different branches. In this thesis, we address the above shortcomings by proposing an ontology-based approach to semantically populate a common knowledge base of processes and cloud resources and thus enable interoperability between organization's branches. We construct our knowledge base built by extending existing ontologies. We thereafter propose an approach to mine such knowledge base to assist the development of BP variants. Furthermore, we adopt a genetic algorithm to optimally allocate cloud resources to BPs. To validate our approach, we develop two proof of concepts and perform experiments on real datasets. Experimental results show that our approach is feasible and accurate in real use-cases

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Yongsiriwit, Karn. "Modeling and mining business process variants in cloud environments." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL002.

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De plus en plus les organisations adoptent les systèmes d'informations sensibles aux processus basés sur Cloud en tant qu'un environnement pour gérer et exécuter des processus dans le Cloud dans l'objectif de partager et de déployer leurs applications de manière optimale. Cela est particulièrement vrai pour les grandes organisations ayant des succursales opérant dans des différentes régions avec des processus considérablement similaires. Telles organisations doivent soutenir de nombreuses variantes du même processus en raison de la culture locale de leurs succursales, de leurs règlements, etc. Cependant, le développement d'une nouvelle variante de processus à partir de zéro est sujet à l'erreur et peut prendre beaucoup du temps. Motivés par le paradigme "la conception par la réutilisation", les succursales peuvent collaborer pour développer de nouvelles variantes de processus en apprenant de leurs processus similaires. Ces processus sont souvent hétérogènes, ce qui empêche une interopérabilité facile et dynamique entre les différentes succursales. Une variante de processus est un ajustement d'un modèle de processus afin de s'adapter d'une façon flexible aux besoins spécifiques. De nombreuses recherches dans les universités et les industries visent à faciliter la conception des variantes de processus. Plusieurs approches ont été développées pour aider les concepteurs de processus en recherchant des modèles de processus métier similaires ou en utilisant des modèles de référence. Cependant, ces approches sont lourdes, longues et sujettes à des erreurs. De même, telles approches recommandent des modèles de processus pas pratiques pour les concepteurs de processus qui ont besoin d'ajuster une partie spécifique d'un modèle de processus. En fait, les concepteurs de processus peuvent mieux développer des variantes de processus ayant une approche qui recommande un ensemble bien défini d'activités à partir d'un modèle de processus défini comme un fragment de processus. Les grandes organisations multi-sites exécutent les variantes de processus BP dans l'environnement Cloud pour optimiser le déploiement et partager les ressources communes. Cependant, ces ressources Cloud peuvent être décrites en utilisant des différents standards de description des ressources Cloud ce qui empêche l'interopérabilité entre les différentes succursales. Dans cette thèse, nous abordons les limites citées ci-dessus en proposant une approche basée sur les ontologies pour peupler sémantiquement une base de connaissance commune de processus et de ressources Cloud, ce qui permet une interopérabilité entre les succursales de l'organisation. Nous construisons notre base de connaissance en étendant les ontologies existantes. Ensuite, nous proposons une approche pour exploiter cette base de connaissances afin de supporter le développement des variantes BP. De plus, nous adoptons un algorithme génétique pour allouer d'une manière optimale les ressources Cloud aux BPs. Pour valider notre approche, nous développons deux preuves de concepts et effectuons des expériences sur des ensembles de données réels. Les résultats expérimentaux montrent que notre approche est réalisable et précise dans des cas d'utilisation réels More and more organizations are adopting cloud-based Process-Aware Information Systems (PAIS) to manage and execute processes in the cloud as an environment to optimally share and deploy their applications. This is especially true for large organizations having branches operating in different regions with a considerable amount of similar processes. Such organizations need to support many variants of the same process due to their branches' local culture, regulations, etc. However, developing new process variant from scratch is error-prone and time consuming. Motivated by the "Design by Reuse" paradigm, branches may collaborate to develop new process variants by learning from their similar processes. These processes are often heterogeneous which prevents an easy and dynamic interoperability between different branches. A process variant is an adjustment of a process model in order to flexibly adapt to specific needs. Many researches in both academics and industry are aiming to facilitate the design of process variants. Several approaches have been developed to assist process designers by searching for similar business process models or using reference models. However, these approaches are cumbersome, time-consuming and error-prone. Likewise, such approaches recommend entire process models which are not handy for process designers who need to adjust a specific part of a process model. In fact, process designers can better develop process variants having an approach that recommends a well-selected set of activities from a process model, referred to as process fragment. Large organizations with multiple branches execute BP variants in the cloud as environment to optimally deploy and share common resources. However, these cloud resources may be described using different cloud resources description standards which prevent the interoperability between different branches. In this thesis, we address the above shortcomings by proposing an ontology-based approach to semantically populate a common knowledge base of processes and cloud resources and thus enable interoperability between organization's branches. We construct our knowledge base built by extending existing ontologies. We thereafter propose an approach to mine such knowledge base to assist the development of BP variants. Furthermore, we adopt a genetic algorithm to optimally allocate cloud resources to BPs. To validate our approach, we develop two proof of concepts and perform experiments on real datasets. Experimental results show that our approach is feasible and accurate in real use-cases

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Bou, nader Ralph. "Enhancing email management efficiency : A business process mining approach." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS017.

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La gestion des processus métier (BPM) est cruciale pour toute organisation cherchant à améliorer constamment ses opérations. Cela implique plusieurs étapes : conception, modélisation, exécution, surveillance, optimisation et automatisation. Un élément central du BPM est l'analyse des processus, qui consiste à examiner les traces d'exécution pour identifier les inefficacités et les déviations par rapport aux processus prévus. Cette analyse se concentre particulièrement sur la prédiction des processus futurs et sur la vérification de leur conformité. Dans cette thèse, nous nous penchons sur les défis spécifiques à l'analyse des processus métier lorsqu'ils sont pilotés par courriel. Il est essentiel de maîtriser ces pratiques pour rationaliser les opérations et maximiser la productivité. La vérification de conformité garantit que les processus réels respectent les modèles prédéfinis, assurant ainsi le respect des normes et standards. Par ailleurs, la prédiction des processus permet d'anticiper le comportement futur des opérations en se basant sur des données historiques, ce qui aide à optimiser l'utilisation des ressources et à gérer efficacement les charges de travail. Appliquer ces techniques aux processus pilotés par courriel présente des défis uniques. En effet, ces processus manquent souvent des modèles formels trouvés dans les systèmes BPM traditionnels, ce qui nécessite des méthodologies adaptées. Les traces d'exécution dérivées des courriels ont une structure particulière, comprenant des attributs tels que les actes de parole des interlocuteurs et les données commerciales pertinentes. Cette complexité rend l'application des méthodes standard de fouille des processus plus difficile. L'intégration de ces attributs dans les techniques existantes de BPM et les systèmes de courriel demande des algorithmes avancés et une personnalisation importante, d'autant plus que le contexte des communications par courriel est souvent dynamique. Pour relever ces défis, cette thèse propose plusieurs objectifs. D'abord, mettre en place une vérification de conformité multi-aspects et concevoir un système de recommandation de réponse par courriel qui tient compte des activités du processus. Ensuite, il s'agit de concevoir un modèle de processus basé sur des contraintes séquentielles et contextuelles spécifiées par un analyste/expert en données. Il est également crucial de développer des algorithmes pour identifier les événements conformes et non conformes, d'utiliser les traces d'exécution pour prédire les connaissances des processus métier et de proposer des modèles de réponse par courrier électronique. Les principes directeurs de cette approche sont la sensibilité au contexte, l'interdisciplinarité, la cohérence, l'automatisation et l'intégration. L'une des contributions majeures de cette étude est le développement d'un logiciel complet pour l'analyse des processus pilotés par courriel. Ce programme combine la prédiction des processus et la vérification de conformité pour améliorer la communication par courriel. Il propose des modèles de réponse adaptés et évalue la conformité des courriels avant leur envoi. Pour valider ce logiciel, des données de courriels réels ont été utilisées, fournissant ainsi une base pratique pour des comparaisons et des recherches futures Business Process Management (BPM) involves continuous improvement through stages such as design, modeling, execution, monitoring, optimization, and automation. A key aspect of BPM is Business Process (BP) mining, which analyzes event logs to identify process inefficiencies and deviations, focusing on process prediction and conformance checking. This thesis explores the challenges of BP mining within email-driven processes, which are essential for streamlining operations and maximizing productivity.Conformance checking ensures that actual process execution aligns with predicted models, maintaining adherence to predefined standards. Process prediction forecasts future behavior based on historical data, aiding in resource optimization and workload management. Applying these techniques to email-driven processes presents unique challenges, as these processes lack the formal models found in traditional BPM systems and thus require tailored methodologies.The unique structure of email-derived event logs, featuring attributes such as interlocutor speech acts and relevant business data, complicates the application of standard BP mining methods. Integrating these attributes into existing business process techniques and email systems demands advanced algorithms and substantial customization, further complicated by the dynamic context of email communications.To address these challenges, this thesis aims to implement multi-perspective conformance checking and develop a process-activity-aware email response recommendation system. This involves creating a process model based on sequential and contextual constraints specified by a data analyst/expert, developing algorithms to identify fulfilling and violating events, leveraging event logs to predict BP knowledge, and recommending email response templates. The guiding principles include context sensitivity, interdisciplinarity, consistency, automation, and integration.The contributions of this research include a comprehensive framework for analyzing email-driven processes, combining process prediction and conformance checking to enhance email communication by suggesting appropriate response templates and evaluating emails for conformance before sending. Validation is achieved through real email datasets, providing a practical basis for comparison and future research

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Schönig, Stefan, Macias Cristina Cabanillas, Ciccio Claudio Di, Stefan Jablonski, and Jan Mendling. "Mining team compositions for collaborative work in business processes." Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/s10270-016-0567-4.

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Process mining aims at discovering processes by extracting knowledge about their different perspectives from event logs. The resource perspective (or organisational perspective) deals, among others, with the assignment of resources to process activities. Mining in relation to this perspective aims to extract rules on resource assignments for the process activities. Prior research in this area is limited by the assumption that only one resource is responsible for each process activity, and hence, collaborative activities are disregarded. In this paper, we leverage this assumption by developing a process mining approach that is able to discover team compositions for collaborative process activities from event logs. We evaluate our novel mining approach in terms of computational performance and practical applicability.

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PESTANA, L. F. "Aplicação do Process Mining na Auditoria de Processos Governamentais." Universidade Federal do Espírito Santo, 2017. http://repositorio.ufes.br/handle/10/8692.

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Made available in DSpace on 2018-08-01T23:38:03Z (GMT). No. of bitstreams: 1 tese_10969_Dissertação Luciana França_Versão Final.pdf: 2442120 bytes, checksum: c6bc5141d2f33e637a2a6eb80e448216 (MD5) Previous issue date: 2017-12-06 A auditoria de processos de negócios é um tema de relevância crescente na literatura. No entanto, técnicas tradicionais e manuais demonstram-se insatisfatórias ou insuficientes, visto que as mesmas são custosas, podem ser tendenciosas e passíveis de erros, além de envolverem grande quantidade de recursos temporais, humanos e materiais. Nesse sentido, o presente estudo vem demonstrar como a técnica de process mining pode ser utilizada, de forma automática, na auditoria de processos governamentais, a partir de um sistema de informação e de uma ferramenta de mining denominada ProM. A partir de técnicas de verificação de conformidade, realizou-se a comparação entre os processos reais e seus respectivos modelos oficiais de uma organização governamental. Os resultados obtidos demonstram algumas divergências entre eles, e indicam que a técnica pode ser utilizada como um meio auxiliar na realização de auditoria de processos de negócios.

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Castellano, Mattia. "Business Process Management e tecniche per l'applicazione del Process Mining. Il caso Università degli Studi di Parma." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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In un mondo in cui l'apertura di nuovi mercati e l'introduzione di nuove tecnologie genera continuamente nuove opportunità, le aziende devono sempre più imparare ad adattarsi e a gestire il cambiamento. Con la diffusione del Process Thinking le organizzazioni iniziano a rendere propri concetti quali processi, attività, eventi, flussi, cambiamento e sviluppo. E' in questo scenario che nascono discipline orientate al cambiamento organizzativo e miglioramento dei processi aziendali, come il Business Process Re-engineering (BPR) e il Business Process Management (BPM). L'aumento della tecnologia e l'era dell'Information Technology (IT), i sistemi informativi assumono un ruolo sempre più importante nella vita dell'organizzazione, supportando l'esecuzione dei processi e iniziando a produrre grandi quantità di tracce relative all'esecuzione dei task. Comincia l'era dei Big Data e del Data Mining. La ricerca arriverà a soddisfare il bisogno aziendale di estrazione di valore tangibile dai dati, o log, con le tecniche di Process Mining. Le tecniche di Process Mining, considerate una forma di Business Intelligence (BI) vengono oggi applicate in vari settori industriali, primo fra tutti il settore dei Servizi. Verrà analizzata nel dettaglio un'applicazione delle tecniche di Process Mining in un progetto commissionato dall'Università degli Studi di Parma a HSPI S.p.A, azienda di consulenza direzionale nella quale ho svolto il tirocinio per Tesi e partecipato attivamente all'analisi. Il Process Mining si conferma una valida tecnica per l'analisi dei dati offline, e la ricerca è attualmente concentrata all'implementazione del Process Mining nell'analisi dei dati real-time, al fine di affrontare la necessità di cambiamento in modo tempestivo, e trarne un vantaggio competitivo.

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Decker, Sebastian. "Data-driven business process improvement : An illustrative case study about the impacts and success factors of business process mining." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-43958.

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The current business environment is rapidly and fundamentally changing. The main driver are digital technologies. Companies face the pressure to exploit those technologies to improve their business processes in order to achieve competitive advantage. In the light of increased complexity of business processes and the existence of corporate Big Data stored in information systems, the discipline of process mining has emerged. Investigate how process mining can support the optimization of business processes. In this qualitative study, an illustrative case study research is utilized involving eight research participants. Hereby, data is primarily collected from semi-structured interviews. The data is analyzed using content analysis. In addition, the illustrative case serves the purpose to demonstrate the application of process mining. The research revealed that process mining has important impacts on current business process improvement. Not all of them were explicitly positive. The derived success factors should support vendors, current and potential users to apply process mining safe and successfully.

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Schönig, Stefan, Macias Cristina Cabanillas, Stefan Jablonski, and Jan Mendling. "A framework for efficiently mining the organisational perspective of business processes." Elsevier, 2016. http://dx.doi.org/10.1016/j.dss.2016.06.012.

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Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included. In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability.

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Bala, Saimir, Jan Mendling, Martin Schimak, and Peter Queteschiner. "Case and Activity Identification for Mining Process Models from Middleware." Springer, Cham, 2018. http://epub.wu.ac.at/6620/1/PoEM2018%2Dsubmitted.pdf.

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Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.

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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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Gonella, Philippe. "Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11799/.

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Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining.

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Bala, Saimir. "Mining Projects from Structured and Unstructured Data." Jens Gulden, Selmin Nurcan, Iris Reinhartz-Berger, Widet Guédria, Palash Bera, Sérgio Guerreiro, Michael Fellman, Matthias Weidlich, 2017. http://epub.wu.ac.at/7205/1/ProjecMining%2DCamera%2DReady.pdf.

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Companies working on safety-critical projects must adhere to strict rules imposed by the domain, especially when human safety is involved. These projects need to be compliant to standard norms and regulations. Thus, all the process steps must be clearly documented in order to be verifiable for compliance in a later stage by an auditor. Nevertheless, documentation often comes in the form of manually written textual documents in different formats. Moreover, the project members use diverse proprietary tools. This makes it difficult for auditors to understand how the actual project was conducted. My research addresses the project mining problem by exploiting logs from project-generated artifacts, which come from software repositories used by the project team.

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Sharma, Sumana. "An Integrated Knowledge Discovery and Data Mining Process Model." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1615.

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Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests indicate that the IKDDM model outperforms the CRISP model in terms of efficiency and effectiveness; the IKDDM model also outperforms CRISP in terms of quality of the process model itself.

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El-Gharib, Najah Mary. "Using Process Mining Technology to Understand User Behavior in SaaS Applications." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39963.

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Processes are running everywhere. Understanding and analyzing business and software processes and their interactions is critical if we wish to improve them. There are many event logs generated from Information Systems and applications related to fraud detection, healthcare processes, e-commerce processes, and others. These event logs are the starting point for process mining. Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs available in information systems. Process mining provides fact-based insight from real event logs that helps analyze and improve existing business processes by answering, for example performance or conformance questions. As the number of applications developed in a cloud infrastructure (often called Software as a Service – SaaS at the application level) is increasing, it becomes essential and useful to study and discover these processes. However, SaaS applications bring new challenges to the problem of process mining. Using the Design Science Research Methodology, this thesis introduces a new method to study, discover, and analyze cloud-based application processes using process mining techniques. It explores the applications and known challenges related to process mining in cloud applications through a systematic literature review (SLR). It then contributes a new Application Programming Interface (API), with an implementation in R, and a companion method called Cloud Pattern API – Process Mining (CPA-PM), for the preprocessing of event logs in a way that addresses many of the challenges identified in the SLR. A case study involving a SaaS company and real event logs related to the trial process of their online service is used to validate the proposed solution.

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Moctar, m'baba Leyla. "Combining Blockchain and IoT for business processes deployment and mining." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS010.

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Blockchain, initialement utilisé en 2009 pour les transactions de cryptomonnaie, a rapidement évolué au-delà des applications financières. La communauté BPM a reconnu son potentiel pour améliorer la gestion des processus métier (BPM) et favoriser les collaborations inter-organisationnelles. Malgré des recherches approfondies sur l'exécution des processus d'affaires basés sur la blockchain, l'exploration de données de la blockchain pour le process mining a récemment commencé à être explorée. Les études actuelles se concentrent principalement sur les processus centrés sur les activités, négligeant souvent les processus centrés sur les artefacts prévalents dans les applications blockchain. Les formats de journalisation traditionnels comme XES, bien que couramment utilisés, rencontrent des défis tels que la perte d'information et la dénormalisation lorsqu'ils sont appliqués à des données centrées sur les artefacts. L'introduction d'OCEL a partiellement abordé ces problèmes en permettant le stockage de données d'événements centrées sur les objets, mais il manque de prise en charge pour l'évolution et les relations des objets.Cette thèse relève ces défis en proposant ACEL, une extension d'OCEL qui prend en charge de manière complète le stockage des données d'événements centrées sur les artefacts. Nous présentons une méthode centrée sur les artefacts pour recueillir des données d'événements d'applications blockchain, les convertissant en logs ACEL. La viabilité de l'approche est évaluée en utilisant les applications Ethereum Cryptokitties et Augur. Nous comparons d'abord les capacités de process mining d'ACEL avec OCEL, puis introduisons une méthode de découverte utilisant le clustering hiérarchique et l'analyse du gain d'information pour dériver des modèles GSM, la norme pour les processus centrés sur les artefacts. Notre évaluation sur Cryptokitties confirme la faisabilité de cette approche et met en évidence les avantages d'ACEL dans le process mining centré sur les artefacts Blockchain, first utilized in 2009 for cryptocurrency transactions, quickly evolved beyond financial applications. The BPM community recognized its potential for enhancing business process management (BPM) and fostering inter-organizational collaborations. Despite extensive research on blockchain-based business process execution, process mining from blockchain data has recently begun to be explored. Current studies mainly focus on activity-centric processes, often overlooking artifact-centric processes prevalent in blockchain applications. Traditional logging formats like XES, while commonly used, face challenges like information loss and denormalization when applied to artifact-centric data. The introduction of OCEL partially addressed these issues by enabling the storage of object-centric event data, but it lacks support for object evolution and relations.This thesis addresses these challenges by proposing ACEL, an extension of OCEL that comprehensively supports artifact-centric event data storage. We present an artifact-centric method to gather event data from blockchain applications, converting them into ACEL logs. The approach's viability is assessed using Cryptokitties and Augur Ethereum applications. We initially compare ACEL's process mining capabilities with OCEL, and then introduce a discovery method using hierarchical clustering and information gain analysis to derive GSM models, the standard for artifact-centric processes. Our evaluation on Cryptokitties confirms the feasibility of this approach and highlights the advantages of ACEL in artifact-centric process mining

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Schieber, Andreas, and Andreas Hilbert. "Entwicklung eines generischen Vorgehensmodells für Text Mining." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-141372.

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Vor dem Hintergrund des steigenden Interesses von computergestützter Textanalyse in Forschung und Praxis entwickelt dieser Beitrag auf Basis aktueller Literatur ein generisches Vorgehensmodell für Text-Mining-Prozesse. Das Ziel des Beitrags ist, die dabei anfallenden, umfangreichen Aktivitäten zu strukturieren und dadurch die Komplexität von Text-Mining-Vorhaben zu reduzieren. Das Forschungsziel stützt sich auf die Tatsache, dass im Rahmen einer im Vorfeld durchgeführten, systematischen Literatur-Review keine detaillierten, anwendungsneutralen Vorgehensmodelle für Text Mining identifiziert werden konnten. Aufbauend auf den Erkenntnissen der Literatur-Review enthält das resultierende Modell daher sowohl induktiv begründete Komponenten aus spezifischen Ansätzen als auch aus literaturbasierten Anforderungen deduktiv abgeleitete Bestandteile. Die Evaluation des Artefakts belegt die Nützlichkeit des Vorgehensmodells im Vergleich mit dem bisherigen Forschungsstand.

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Polańska, Julia, and Michał Zyznarski. "Elaboration of a method for comparison of Business Intelligence Systems which support data mining process." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2078.

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Business Intelligence Systems are becoming more and more popular in recent years. It is caused by the need of reusing data in order to gain some potentially useful business information about. Those systems are advanced set of tools, which causes high prices of purchase and licensing. Therefore, it is important to choose the system which fits the best particular business needs. The aim of this thesis is to elaborate a method for comparison of existing Business Intelligence Systems that are supporting data mining. The method consist of a quality model, build according to existing standards, and set of steps which should be taken to choose a Business Intelligence System according to particular requirements of its future user. The first part of the thesis focuses on the analysis of existing works providing a way for comparison of those software products. It is shown here that there is no existing systematic approach resolving this problem. However, criteria presented in those works along with the description of quality model standards were used for creating the quality model and proposing a set of basic measures. Also the phrases for the evaluation process were identified. The next part of the research is a case study which purpose is to show the usefulness of proposed evaluation method. The example is simple, but has proven that the method can be easily modified for specific needs and used for comparison of real Business Intelligence Systems. The quality level measured in the case study turned out to be very similar for each system. The evaluation method may be extended in future work with more advanced measures or additional characteristic which were not taken into account in this research.

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Evangelista, Pescorán Misael Elias, and Torres Andre Junior Coronado. "Modelo para la evaluación de variables en el Sector Salud utilizando Process Mining y Data Visualization." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/653132.

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El presente trabajo propone un modelo para la evaluación de variables en el sector salud utilizando Process Mining y Data Visualization soportado por la herramienta Celonis. Esto surge ante la problemática orientada a la dificultad en la comprensión de las actividades que están involucradas en los procesos negocios y los resultados de este. El proyecto se centra en la investigación de dos disciplinas emergentes. Una de estas disciplinas es Process Mining y se enfoca principalmente en los procesos, en los datos por cada evento, esto con el fin de descubrir un modelo, ver conformidad de los procesos o mejorarlos (Process Mining: Una técnica innovadora para la mejora de los procesos, 2016). La segunda disciplina es Data Visualization, esta permite presentar los datos en un formato gráfico o pictórico ("Data Visualization: What it is and why it matters", 2016). El proyecto implica principalmente investigación, en primer lugar, se analizan las técnicas de Process Mining y Data Visualization. En segundo lugar, se separan las características y cualidades de las disciplinas, y se diseña un modelo para la evaluación de variables en el Sector Salud utilizando Process Mining y Data Visualization, generando un valor agregado, dado que al tener un formato gráfico o pictórico que representa adecuadamente los resultados de usar una técnica de minería de procesos, la comprensión y el análisis en la toma de decisiones es más precisa. En tercer lugar, se valida el modelo en una institución que brinda servicios en el Sector Salud, analizando uno de los procesos core. Finalmente, se elabora un plan de continuidad para que el modelo propuesto se aplique en técnicas de optimización de procesos en las organizaciones. The present work proposes a model for the evaluation of variables in the health sector using Process Mining and Data Visualization supported by the Celonis tool. This arises from the problem oriented to the difficulty in understanding the activities that are involved in business processes and their results. The project focuses on the investigation of two emerging disciplines. One of these disciplines is Process Mining and it focuses mainly on the processes, on the data for each event, this in order to discover a model, see conformity of the processes or improve them (Process Mining: An innovative technique for the improvement of the processes, 2016). The second discipline is Data Visualization, this allows data to be presented in a graphic or pictorial format ("Data Visualization: What it is and why it matters", 2016). This project mainly involves research, first, Process Mining and Data Visualization techniques are analyzed. Second, the characteristics and qualities of the disciplines are separated, and a model is designed for the evaluation of variables in the Health Sector using Process Mining and Data Visualization, generating added value, given that by having a graphic or pictorial format that adequately represents the results of using a process mining technique, understanding and analysis in decision making is more accurate. Third, the model is validated in an institution that provides services in the Health Sector, analyzing one of the core processes. Finally, a continuity plan is drawn up so that the proposed model can be applied to process optimization techniques in organizations. Tesis

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Reguieg, Hicham. "Using MapReduce to scale event correlation discovery for process mining." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2014. http://tel.archives-ouvertes.fr/tel-01002623.

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The volume of data related to business process execution is increasing significantly in the enterprise. Many of data sources include events related to the execution of the same processes in various systems or applications. Event correlation is the task of analyzing a repository of event logs in order to find out the set of events that belong to the same business process execution instance. This is a key step in the discovery of business processes from event execution logs. Event correlation is a computationally-intensive task in the sense that it requires a deep analysis of very large and growing repositories of event logs, and exploration of various possible relationships among the events. In this dissertation, we present a scalable data analysis technique to support efficient event correlation for mining business processes. We propose a two-stages approach to compute correlation conditions and their entailed process instances from event logs using MapReduce framework. The experimental results show that the algorithm scales well to large datasets.

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Dayan, Imran. "A method for measuring Internal Fraud Risk (IFR) of business organisations with ERP systems." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/17556.

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ERP system has shaped the way modern organisations design, control, and execute business processes. It has not only paved the way for efficient use of organisational resources but also offered the opportunity to utilise data logged in the system for ensuring internal control. The key contribution of this research is that it has resulted in a method which can practically be employed by internal auditors for measuring internal fraud risk of business organisations with ERP systems, by utilising process mining technique and evidential reasoning in the form of Bayesian theorem, in a much more effective way compared to conventional frequentist method. The other significant contribution is that it has paved the way for combining process mining technique and evidential reasoning in addressing problems prevalent within organisational contexts. This research has contributed in developing IS theories for design and action especially in the area of soft systems methodology as it has relied on business process modelling in addressing the issue of internal fraud risk. The chosen method has contributed in facilitating incorporation of design science method in problem solving. Researchers have focused on applying data mining techniques within organisational contexts for extracting valuable information. Process mining is a comparatively new technique which allows business processes to be analysed based on event logs. Analysis of business processes can be useful for organisations not only for attaining greater efficiency but also for ensuring internal control inside the organisation. Large organisations have various measures in place for ensuring internal control. Measuring the risk of fraud within a business process is an important practice for preventing fraud as accurate measurement of fraud risk provides business experts with the opportunity to comprehend the extent of the problem. Business experts, such as internal auditors, still heavily rely upon conventional methods for measuring internal fraud risk way by of random check of process compliance. Organisations with ERP systems in place can avail themselves of the opportunity to use event logs for extending the scope of assessing process conformance. This has not been put into practice as there is a lack of well researched methods which can allow event logs to be utilised for enhancing internal control. This research can be proved to be useful for practitioners as it has developed a method for measuring internal fraud risk within organisations. This research aimed to utilise process mining technique that allows business experts to exert greater control over business process execution by allowing the internal fraud risk to be measured effectively. A method has been developed for measuring internal fraud risk of business originations with ERP systems by using process mining and Bayesian theorem. In this method, rate of process deviation is calculated by conducting process mining on relevant logs of events and then that process deviation rate is applied in Bayesian theorem along with historic internal fraud risk rate and process deviation rate calculated manually for arriving at a revised internal fraud risk rate. Bayesian theorem has been relied upon for the purpose of developing this new method as it allows evidential reasoning to be incorporated. The method has been developed as a Design Science Research Method (DSRM) artefact by conducting three case-studies. Data has been collected from three case companies, operating in readymade garments manufacturing industry, pharmaceuticals industry, and aviation industry, regarding their procurement process for conducting process mining. The revised internal fraud risk rates were then evaluated by considering the feedback received from respective business experts of each of the case company. The proposed method is beneficial as it has paved the way for practitioners to utilise process mining using a soft system methodology. The developed method is of immense significance as it has contributed in the field of business intelligence and analytics (BI&A) and the big data analytics which have become significantly important to both academics and practitioners over the past couple of decades.

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Verenich, Ilya. "Explainable predictive monitoring of temporal measures of business processes." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/124037/1/Ilya_Verenich_Thesis.pdf.

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This thesis explores data-driven, predictive approaches to monitor business process performance. These approaches allow process stakeholders to prevent or mitigate potential performance issues or compliance violations in real time, as early as possible. To help users understand the rationale for the predictions and build trust in them, the thesis proposes two techniques for explainable predictive process monitoring: one based on deep learning, the other driven by process models. This is achieved by decomposing a prediction into its elementary components. The techniques are compared against state-of-the-art baselines and a trade-off between accuracy and explainability of the predictions is evaluated.

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Singh, Rahul. "A model to integrate Data Mining and On-line Analytical Processing: with application to Real Time Process Control." VCU Scholars Compass, 1999. https://scholarscompass.vcu.edu/etd/5521.

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Since the widespread use of computers in business and industry, a lot of research has been done on the design of computer systems to support the decision making task. Decision support systems support decision makers in solving unstructured decision problems by providing tools to help understand and analyze decision problems to help make better decisions. Artiﬁcial intelligence is concerned with creating computer systems that perform tasks that would require intelligence if performed by humans. Much research has focused on using artiﬁcial intelligence to develop decision support systems to provide intelligent decision support. Knowledge discovery from databases, centers around data mining algorithms to discover novel and potentially useful information contained in the large volumes of data that is ubiquitous in contemporary business organizations. Data mining deals with large volumes of data and tries to develop multiple views that the decision maker can use to study this multi-dimensional data. On-line analytical processing (OLAP) provides a mechanism that supports multiple views of multi-dimensional data to facilitate efficient analysis. These two techniques together can provide a powerful mechanism for the analysis of large quantities of data to aid the task of making decisions. This research develops a model for the real time process control of a large manufacturing process using an integrated approach of data mining and on-line analytical processing. Data mining is used to develop models of the process based on the large volumes of the process data. The purpose is to provide prediction and explanatory capability based on the models of the data and to allow for efﬁcient generation of multiple views of the data so as to support analysis on multiple levels. Artiﬁcial neural networks provide a mechanism for predicting the behavior of nonlinear systems, while decision trees provide a mechanism for the explanation of states of systems given a set of inputs and outputs. OLAP is used to generate multidimensional views of the data and support analysis based on models developed by data mining. The architecture and implementation of the model for real-time process control based on the integration of data mining and OLAP is presented in detail. The model is validated by comparing results obtained from the integrated system, OLAP-only and expert opinion. The system is validated using actual process data and the results of this veriﬁcation are presented. A discussion of the results of the validation of the integrated system and some limitations of this research with discussion on possible future research directions is provided.

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Thom, Lucinéia Heloisa. "A pattern-based approach for business process modeling." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/8512.

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Organizações modernas apresentam demandas relacionadas à automação dos seus processos de negócio devido à alta complexidade dos mesmos e à necessidade de maior eficácia na execução. Neste contexto, a tecnologia de workflow tem se mostrado bastante eficiente, principalmente para a automatização dos processos de negócio. No entanto, por ser uma tecnologia emergente e em evolução, workflow apresenta algumas limitações. Ainda que diversos (meta) modelos de workflow tenham sido propostos nos últimos, anos, seus sub-modelos para representação dos aspectos estruturais da organização apresentam baixo poder de expressão. Além disso, a maioria das ferramentas para modelagem de workflow não provêm funcionalidades para definição, consulta e reuso de padrões. Um dos principais problemas é falta de um mapeamento consolidado entre padrões de funções recorrentes em processos de negócio (ex: solicitação de execução de atividade, aprovação de documentos) e (meta) modelos e/ou ferramentas para modelagem de processos de negócio e workflow. Além disso, a maioria das abordagens em padrões de workflow não exploram a completude e necessidade dos seus padrões para modelagem de workflow. A primeira contribuição desta tese é um Modelo Transacional de Processos de Negócio (MTPN) com suporte aos aspectos estruturais da organização. O metamodelo possibilita a criação de (sub-)processos de negócio a partir do reuso de padrões, principalmente com base nestes aspectos. Adicionalmente, o metamodelo sugere a geração automática de padrões através da Linguagem de Execução para Web Services (BPEL4WS). Outra importante contribuição da tese é um conjunto de padrões de workflow representados como atividades de bloco. Cada padrão descreve uma função recorrente em processos de negócio. A mineração de 190 processos de workflow de mais de 10 organizações diferentes provou a existência dos padrões com alto suporte nos processos de workflow analisados. Além disso, o estudo mostrou que o conjunto de padrões é suficiente e necessário para modelar todos os 190 processos investigados. O estudo também resultou em um conjunto de regras de associação. As regras não apenas contribuem para uma melhor definição dos padrões de atividade de bloco, mas também para a combinação destes com padrões de controle de fluxo. Modern organizations have demands related to the automation of their business processes since such processes are highly complex and need to be efficiently executed. Within this context, the workflow technology has shown to be very effective, mainly in the business process automation. However, as it is an emergent technology and in constant evolution, workflow presents some limitations. Though several workflow (meta) models have been proposed in recent years, their sub-models for organizational structure aspects representation show limited power of expression. On the other hand, most of the current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. One of the main problems is the non-availability of a consolidated mapping between patterns based on recurrent functions found in business processes (e.g., request for activity execution, notification, decision, or approval) and workflow (meta) models or workflow modeling tools. Relying on these problems, the first contribution of this thesis is a Transactional Metamodel of Business Process (TMBP) with support to organizational structure aspects. The metamodel makes feasible to create business (sub-)processes from the reuse of organizational –based workflow patterns. An additional feature of TMBP supports the generation of business (sub-)processes through the Business Process Execution Language for Web Services (BPEL4WS). Other important contribution of this thesis is a set of workflow patterns represented as block activity patterns. Each pattern refers to a recurrent business function frequently found in business processes. The mining of 190 workflow processes of more than 10 different organizations has evidenced the existence of the set of workflow patterns with high support in the workflow processes analyzed. Moreover, it became clear through this study that the set of patterns is both necessary and enough to design all 190 processes that were investigated. As a consequence of the mining process, a set of association rules was identified too. The rules not only help to better define specific workflow patterns, but also combine them with existent control flow patterns. These rules can be useful for building more complex workflows.

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Elleuch, Marwa. "Business process discovery from emails, a first step towards business process management in less structured information systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS014.

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La fouille de processus vise à analyser les traces d'exécution des systèmes d'information (SI), utilisés dans le cadre des activités métiers, pour découvrir des connaissances sur les processus métiers (PM). D'importants travaux de recherche ont été menés dans ce domaine. Cependant, ils supposent généralement que ces traces d'exécution ont un niveau de structuration élevé. Cela signifie que: (i) ils sont composés d'enregistrements structurés, chacun capturant l'exécution d'une activité, et (ii) une partie des attributs des événements d'exécution (comme le nom de l'activité, l'horodatage) sont explicitement inclus dans ces enregistrements, ce qui facilite leur inférence. Néanmoins, les PM peuvent être entièrement ou partiellement réalisés dans des SI moins structurés générant des traces d’exécution de faible niveau de structuration. Les systèmes de courriels sont largement utilisés pour réaliser de manière collaborative des activités de PM. Cependant, leurs traces d’exécution sont de nature non-structurée de point de vue découverte des PM, ce qui empêche l’application directe des techniques existantes. Pour celles qui découvrent les PM à partir des courriels, elles: (i) nécessitent généralement une intervention humaine, et (ii) se sont limitées à la découverte des PM selon la perspective comportementale. Dans cette thèse, nous proposons de découvrir des fragments de PM à partir des courriels selon leurs perspectives fonctionnelles, données, organisationnelles et comportementales. Nous formalisons d'abord ces perspectives en considérant les spécificités des systèmes de courriels. Nous introduisons la notion de contribution des acteurs à la réalisation des activités pour enrichir les perspectives organisationnelles et comportementales. Nous considérons en outre les entités informationnelles manipulées par les activités de PM pour décrire la perspective des données. Pour automatiser la découverte de l’ensemble des perspectives, nous introduisons une approche complètement non-supervisée. Cette approche transforme principalement les traces non structurées des courriels en un journal d'événements structuré avant de l'analyser pour découvrir les PM selon différentes perspectives. Nous introduisons dans ce contexte un ensemble de solutions algorithmiques pour: (i) l'apprentissage non supervisé des activités basé sur la découverte de motifs fréquents de mots dans les courriels, (ii) la découverte des occurrences des activités dans les emails pour capturer les attributs des événements, (iii) la découverte des actes de parole des expéditeurs pour reconnaître leurs intentions de mentionner les activités dans les emails afin de déduire leurs contributions dans leur réalisation, (iv) le regroupement par chevauchement des activités pour découvrir leurs artefacts manipulés (c.-à-d. les entités informationnelles), et (v) la découverte des contraintes séquentielles entre les types d'événements pour découvrir la perspective comportementale des PM. Notre approche est validée en utilisant des courriels publics d’Enron. Nos résultats sont en outre rendus publics pour assurer la reproductibilité dans le domaine étudié. Nous montrons enfin l'utilité de nos résultats pour améliorer la gestion des PM à travers deux applications: (i) un outil de découverte et de recommandation des connaissances de PM à intégrer dans un système de gestion de courriels, et (ii) l'analyse de données CRM pour l'exploration des raisons de la satisfaction/non-satisfaction des utilisateurs Process discovery aims at analysing the execution logs of information systems (IS), used when performing business activities, for discovering business process (BP) knowledge. Significant research works has been conducted in such area. However, they generally assume that these execution logs are of high or of middle level of maturity w.r.t BP discovery. This means that (i) they are composed of structured records while each one captures evidence of one activity execution, and (ii) a part of events’ attributes (e.g. activity name, timestamp) are explicitly included in these records which facilitates their inference. Nevertheless, BP can be entirely or partially performed through less structured IS generating execution logs of low level of maturity. More precisely, emailing systems are widely used as an alternative tool to collaboratively perform BP tasks. Traditional BP discovery techniques could not be applied or at least not directly applied due to the unstructured nature of email logs data. Recently, there have been several initiatives to extend the scope of BP discovery to consider email logs. However, most of them: (i) mostly require human intervention, and (ii) were limited to BP discovery according to its behavioral perspective. In this thesis, we propose to discover BP fragments from email logs w.r.t their functional, data, organizational and behavioral perspectives. We first formalize these perspectives considering emailing systems specifities. We introduce the notion of actors’ contributions towards performing activities to enrich the organizational and the behavioral perspectives. We additionally consider the informational entities manipulated by BP activities to describe the data perspective. To automate their discovery, we introduce a completely unsupervised approach. This approach mainly transforms the unstructured email log into a structured event log before mining it for discovering BP w.r.t multiple perspectives. We introduce in this context several algorithmic solutions for: (i) unsupervised learning activities based on discovering frequent patterns of words from emails, (ii) discovering activity occurrences in emails for capturing event attributes, (iii) discovering speech acts of activity occurrences for recognizing the sender purposes of including activities in emails, (iv) overlapping clustering of activities to discover their manipulated artifacts (i.e. informational entities), and (v) mining sequencing constraints between event types to discover BP behavioral perspective. We validated our approach using emails from the public dataset Enron to show the effectiveness of the obtained results. We publically provide these results to ensure reproducibility in the studied area. We finally show the usefulness of our results for improving BPM through two potential applications: (i) a BP discovery & recommendation tool to be integrated in emailing systems, and (ii) CRM data analysis for mining reasons of users’ satisfaction/non-satisfaction

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Low, Wei Zhe. "Towards cost model-driven log-based business process improvement." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/97727/1/Wei%20Zhe_Low_Thesis.pdf.

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This doctoral study focused on analysing business process execution histories to initiate evidence-based business process improvement activities. The researcher developed techniques to explore and visualise better ways of executing a business process, as well as to analyse the impact of the changes towards cost reduction. This research enables organisations to gain a better understanding of how the same business process can be performed in a more efficient manner, taking into consideration the trade-offs between processing time, cost, and employee utilisation.

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Shetty, Bhupesh. "Process pattern mining: identifying sources of assignable error using event logs." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6641.

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This thesis examines the problem of identifying patterns in process event logs that are correlated with binary events that are undetected until the end of the process. Specifically, we consider the task of identifying patterns in a machine shop manufacturing process that are correlated with product defect. We introduce a pattern mining algorithm based on Apriori to identify frequent patterns, and use binary correlation measures to identify patterns associated with elevated defect rate. We design a simulation model to generate synthetic datasets to test our algorithm. We compare the effectiveness of different correlation measures, target pattern complexities, and sample sizes with and without knowledge of the underlying process. We show that knowledge of the underlying process helps in identifying the pattern that is associated with defects. We also develop a decision support tool based on p-value simulation to help managers identify sources of error in real-life settings. Finally, we apply our method to real world data and extract useful information from the data to help plant managers make decisions related to investments and workforce planning. This thesis also explores the problem of predicting the defect probability given an ordered list of events and its defect status. We develop a supervised learning model using the frequency of patterns deduced from the event log as the feature set. We discuss the challenges faced in this approach and conclude that random forest algorithm performs better than other methods. We apply this approach to a real world case study and discuss the applications in the machine shop. Finally, the thesis explores the order-bidding process in the machine shop industry, and proposes an optimization-based model to maximize the profit of the machine shop. Through a case study example, we show the advantages of using the defect probability in the proposed optimization model to determine the machine-worker schedule to execute job orders in a machine shop.

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Sun, Yaguang [Verfasser], and Bernhard [Akademischer Betreuer] Bauer. "Mining High-Quality Business Process Models from Real-Life Event Logs / Yaguang Sun ; Betreuer: Bernhard Bauer." Augsburg : Universität Augsburg, 2018. http://d-nb.info/115333903X/34.

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García, Oliva Rodrigo Alfonso, and Barrenechea Jesús Javier Santos. "Modelo de evaluación de métricas de control para procesos de negocio utilizando Process Mining." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/653470.

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Este proyecto tiene como objetivo analizar la complejidad de los procesos de negocio en las empresas retail de una forma profunda que en otras técnicas resulta muy difícil o incluso imposible de realizar. Con Process Mining es posible superar esta brecha y eso es lo que queremos demostrar a través de la implementación de un modelo. El proyecto propone un modelo de Process Mining que contemple la presencia de diversas fuentes de información de un proceso logístico en una empresa minorista, así como la aplicación de las tres fases de Process Mining (Descubrimiento, Conformidad y Mejora) y adicionalmente se propone una fase de diagnóstico la cual detalla un conjunto de métricas de control para evaluar el proceso de logística y así poder generar una plan de mejora que dé las pautas para optimizar el proceso en base a lo analizado mediante esta técnica. El modelo desarrollado se implementó en una empresa peruana del sector retail (TopiTop S.A) para el análisis del proceso de logística, específicamente el de gestión de órdenes de compra. Este se analizó dando como resultado de la aplicación del modelo y de la evaluación de las métricas propuestas, la identificación de anomalías en el proceso a través de la aplicación de cada una de las fases del modelo propuesto, asegurando la calidad del análisis en la fase de preprocesamiento, generando el modelo de procesos y extrayendo información que se derivó en métricas de control a través de la herramienta de código abierto ProM Tools. This project aims to analyze the complexity of business processes in retail companies in a deep way that in other techniques is very difficult or even impossible to do. With Process Mining it is possible to overcome this gap and that is what we want to demonstrate through the implementation of a Process Mining model. The project proposes a Process Mining model that contemplates the presence of various sources of information of a logistic process in a retail company, as well as the application of the three phases of Process Mining (Discovery, Compliance and Improvement). Additionally, a diagnostic phase is proposed, which details a set of control metrics to evaluate the logistic process and thus be able to generate an improvement plan that gives the guidelines to optimize the process based on what has been analyzed through this technique. The model developed was implemented in a peruvian company in the retail sector (TopiTop S.A.) for the analysis of the logistics process, specifically the management of purchase orders. This was analyzed giving as a result of the application of the model and the evaluation of the proposed metrics, the identification of anomalies in the process through the application of each of the phases of the proposed model, ensuring the quality of the analysis in the pre-processing phase, generating the process model and extracting information that was derived in control metrics through the open source tool ProM Tools. Tesis

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Taymouri, Farbod. "Light methods for conformance checking of business processes." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/664708.

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Conformance Checking is a new research discipline devoted to identify deviations between business process models and their real executions. Identifying deviations boils down to the notion of alignment conceptually. An alignment quantifies to what degree a process model can imitate what happened in its observed behavior, i.e., an event log. Accordingly, an optimal alignment is the best combination by which the process model can imitate the corresponding observed behavior. The state of the art technique for alignment computation has exponential time and space complexity, hindering its applicability for medium and large instances. The main aim of this thesis is to propose light and efficient methods for alignment computation. By finding a suitable trade-off between computation time, memory consumption and optimality, a familly of techniques is proposed such that depending on the input assumptions and required guarantees, a user can select the right technique for her particular problem. Generally speaking, the methods presented in this thesis can be categorized as: - Classical approaches: These techniques exploit Integer Linear Programming (ILP), as well as structural theory of Petri nets, to formulate alignment computation as an optimization of a set of linear equations. A modification to this strategy which trades-off between complexity and quality is to integrate it with state of the art approach. - Heuristic approaches: These techniques take advantages of heuristic functions to explore the search space of alignments, to find the optimal one(s). This can be done by obtaining an initial solution, and iteratively improving it until saturation or reaching a certain criterion. Another contribution is by adopting a Genetic Algorithm with well specific designed operators, by which exploration of the corresponding search space can be speed up toward the best solution(s). - Model reduction: An alternative way to boost the effectiveness of alignment computation is by reducing model and observed behavior without loosing alignment information. This structure reduction not only boosts the alignment computation, but also provides a big picture of detected deviations. Above that, a divide-and-conquer strategy will be provided for the ILP approach, such that it breaks the original problem into a set of smaller independent problems that can be solved independently. Experiments witness the merit of proposed approaches with respect to state of the art technique in different perspectives, such as resource consumption, execution time, quality and accuracy of the solutions found. All methods have been implemented as a stand-alone tool box called ALI. Conformance checking és una nova disciplina dedicada a identificar desviacions entre els models de processos de negoci i les seves execucions reals. Identificar les desviacions porta directament al concepte d'alineament. Un alineament quantifica el grau en que un model de procés pot imitar el que va passar en el seu comportament observat, és a dir, un registre d'esdeveniments. En conseqüència, una alineament òptim és la millor combinació per la qual el model de procés pot imitar el comportament observat. La tècnica de referència per a la computació d'alineaments té una complexitat exponencial, el que dificulta la seva aplicabilitat per a casos mitjans o grans. L'objectiu principal d'aquesta tesi és proposar mètodes eficients per a la computació d'alineaments. En trobar un punt raonable entre el temps d'execució, el consum de memòria i la optimalitat, es proposa una família de tècniques de manera que, segons els supòsits d'entrada i les garanties requerides, un usuari pot seleccionar la tècnica adequada per al seu problema. En termes generals, els mètodes presentats en aquesta tesi es poden classificar com: Enfocaments clàssics: aquestes tècniques utilitzen la Programació Lineal Entera (anglès, ILP), així com la teoria estructural de les xarxes Petri, per realitzar la computació d'alineaments com una optimització d'un conjunt d'equacions lineals. Una modificació d'aquesta estratègia, que pondera la complexitat i la qualitat, és la d'integrar-la amb l'enfocament de referència. Aproximacions heurístiques: aquestes tècniques aprofiten funcions heurístiques per explorar l'espai de cerca d'alineaments, per trobar les solucions properes a l'òptim. Això es pot fer obtenint una solució inicial, que es millorarà iterativament fins a la saturació, o bé assolint un criteri determinat de convergència. Una altra contribució és l'adopció d'un algoritme evolutiu amb operadors específics, que permeten guiar l'exploració de l'espai de cerca corresponent cap a les millors solucions. Reducció de models: una forma alternativa de potenciar l'efectivitat de la computació d'alineaments és reduint el comportament modelat i observat sense perdre la informació d'alineaments. Aquesta reducció d'estructura no només alleugereix el problema, sinó que també proporciona una visió abstracta de les desviacions detectades. Addicionalment, es proposa una estratègia de divideix i vèncer per a l'enfocament de l'ILP, que trenca el problema original en un conjunt de problemes independents més petits que es poden resoldre de forma independent. Els experiments realitzats per cada tècnica demostren la capacitat dels algorismes proposats, en diferents perspectives, com ara el consum de recursos, el temps d'execució, la qualitat i la precisió de les solucions trobades. Tots els mètodes s'han implementat en el software open-source ALI.

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Marseglia, Matteo Celestino. "Process mining a supporto del service design - il caso di un'azienda di servizi." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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L’utilizzo di tool di Business Process Management(BPM) è realtà in quasi tutte le organizzazioni e la necessità di innovare e dimensionare i processi è un problema sempre più frequente. Questo elaborato ha lo scopo di dimostrare la connessione tra il process mining e il service design. Il process mining (PM) è un tool di BPM che consente di analizzare i processi aziendali partendo dalle informazioni presenti nel sistema informativo. Attraverso il PM si innestano tematiche di Service Design e vengono analizzati e discussi i punti di contatto tra le due discipline. Per provare l’ipotesi che il PM possa risultare utile nel Service Design viene analizzato un case-study applicativo di un’azienda di servizi. Il case-study porta informazioni reali totalmente anonimizzate. Il PM porta alla costruzione di un grafo di processo che consente l’analisi dei flussi e delle attività svolte. I risultati applicativi suggeriscono un vero apporto nell’attività di service design del PM. Infatti, attraverso lo strumento di BPM, è possibile analizzare alcuni touch-point tra cliente e azienda. Oltre a ciò, il PM permette un’analisi demografica della clientela: sono mostrate indicazioni di età, genere, provenienza della clientela e sono analizzati tempi e caratteristiche incrociando i dati stessi. Si evidenzia quindi come queste caratteristiche possiedono informazioni utili alla creazione di personas da parte dell’organizzazione. In ultimo si mostra il PM come strumento di controllo dei processi chiave di erogazione di servizio attraverso lo studio dei tempi dei processi e stagionalità. I risultati suggeriscono una vera utilità dello strumento nel campo del dimensionamento di servizio. Con queste prove il process mining non è uno strumento limitato alla gestione di processi ma risulta utile anche in attività di design del servizio.

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Kratsch, Wolfgang [Verfasser], and Maximilian [Akademischer Betreuer] Röglinger. "Data-driven Management of Interconnected Business Processes : Contributions to Predictive and Prescriptive Process Mining / Wolfgang Kratsch ; Betreuer: Maximilian Röglinger." Bayreuth : Universität Bayreuth, 2021. http://d-nb.info/122950544X/34.

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Baier, Thomas, Ciccio Claudio Di, Jan Mendling, and Mathias Weske. "Matching events and activities by integrating behavioral aspects and label analysis." Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/s10270-017-0603-z.

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Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

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Rojas, Candio Piero Gilmar, and Pasapera Arturo Alonso Villantoy. "Método de evaluación de variables e indicadores para el proceso de Bloque de Cirugía utilizando Process Mining y Data Visualization." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2021. http://hdl.handle.net/10757/655800.

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El Seguro Social de Salud, EsSalud, es un organismo público descentralizado que tiene como finalidad dar cobertura a los asegurados y sus derechohabientes, a través de otorgamiento de diferentes tipos de seguro a la población ante los riesgos humanos [1]. Esta institución brinda atención a un aproximado de 11 millones 493 mil peruanos asegurados, quienes representan un 35,7% de la población total [2], estos asegurados se encuentran concentrados en mayor proporción en edades de 0 a 14 años, de 25 a 44 años y 65 a más [3]. Según la memoria anual realizada en el 2019 por esta institución, muestra que se tiene un total de 28149 reclamos registrados en el sistema de información de Atención al Asegurado, que representa un 18,8 % del total de solicitudes de dicho sistema con un tiempo de conclusión de 25 días [4]. Asimismo, según el diario El Comercio, afirman que uno de los principales motivos de los reclamos se debe a la falta de acceso a los servicios de salud debido al tiempo de espera de atención para los asegurados [5]. EsSalud busca proponer estrategias para reducir estos reclamos y tiempos de espera, pero dichas investigaciones implican un mayor esfuerzo laboral y uso de recursos humanos. Los resultados de las pruebas no muestran ser muy efectivos dado que podría seguir presentando la disconformidad de los asegurados porque el tiempo de atención sigue siendo alto [6]. En este sentido, el presente trabajo consiste en proponer un método que permita contribuir a la mejora y optimización de la toma de decisiones por parte del equipo médico del Bloque de Cirugía sobre su proceso. Nuestro trabajo propone un método que permita formular y evaluar indicadores de Process Mining a través de preguntas relacionadas al funcionamiento de un proceso y permita comprender de manera sencilla las variables del proceso a través de técnicas de Data Visualization. El aporte se encuentra en la definición de variables dentro de las técnicas de Data Visualization. Este tiene el objetivo de permitir comprender en profundidad qué es lo que se va a representar gráficamente y, a la vez, sea de interés a los responsables del proceso de Bloque de Cirugía a nivel de negocio. Nuestra propuesta identifica cuellos de botella y violaciones de políticas de un proceso crítico en una organización de salud, ya que resulta complicado realizar mediciones y análisis para mejorar la calidad y transformación de los procesos en instituciones de atención en el sector salud. Para llevar a cabo el proyecto se tomará como referencia la información de la empresa AUNA. El método se ejecutó a través de escenarios operativos en el proceso quirúrgico de esta red de clínicas para responder preguntas típicas y frecuentes del proceso de Bloque de Cirugía. Se revisaron 1710 casos con un total de 15390 encuentros quirúrgicos. Asimismo, la aplicación del método permitió validar y mejorar el modelo de proceso documentado respecto a los registros de eventos de los sistemas de información del centro de salud. Se identificaron oportunidades de mejora para facilitar los registros de marcado y maximizar la calidad de los resultados para futuros proyectos de Minería de Procesos y Visualización de Datos. Finalmente, la aplicación del método permitió identificar cuellos de botella, variantes, violaciones y varianzas del proceso mediante el uso de indicadores de Minería de Procesos y de variables en Visualización de Datos para comprender el rendimiento actual del Bloque de Cirugía y, posteriormente, tomar decisiones y acciones de mejora en dicho proceso por parte del equipo médico. The Social Health Insurance, EsSalud, is a decentralized public body whose purpose is to provide coverage to the insured and their beneficiaries, through the granting of different types of insurance to the population against human risks [1]. This institution provides care to an approximate 11 million 493 thousand insured Peruvians, who represent 35.7% of the total population [2], these insured persons are concentrated in a greater proportion in ages 0 to 14 years, from 25 to 44 years and 65 and over [3]. According to the annual report carried out in 2019 by this institution, it shows that there is a total of 28,149 claims registered in the Insured Service information system, which represents 18.8% of the total requests of said system with a time of 25-day conclusion [4]. Likewise, according to the newspaper El Comercio, they affirm that one of the main reasons for the complaints is due to the lack of access to health services due to the waiting time for the insured [5]. EsSalud seeks to propose strategies to reduce these claims and waiting times, but these investigations imply a greater work effort and use of human resources. The results of the tests do not show to be very effective since it could continue to present the dissatisfaction of the insured because the time of attention is still high [6]. In this sense, the present work consists in proposing a method that allows to contribute to the improvement and optimization of decision-making by the medical team of the Surgery Block regarding its process. Tesis

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Hansson, Sandra. "Is Self-Service Business Intelligence a hoax? : A descriptive study of casual users’ independence using SSBI in the data mining process." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106081.

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When using Business Intelligence (BI), organizations can improve decision-making bycompiling, understanding and utilizing the data held by the organization. Self-serviceBusiness Intelligence (SSBI) has emerged as a new focus within BI and aims to make BI tools available to business users, and relieve IT-experts involvement of in ad hoc reporting andanalysis. The aim of this research is to examine challenges of casual users being self-reliant when using SSBI tools, and the way they are using them in the data mining process. This wasdone through a qualitative study, interviewing seven individuals from different user groups: casual users, power user and IT-experts. From the results it appears that most casual users are not sufficiently self-reliant in the data mining process using SSBI. The visualization is theprime area for SSBI, which most casual users manage themselves if the data and dashboards are pre-defined for them. SSBI is becoming increasingly more common, which leads to moreand more casual users with increased experience who need to be able to dig out their own data for interpretation and analysis. Yet, without additional knowledge, such as data knowledge or SQL skill, casual users are in need of support when it comes to more complex operations thanad hoc analysis and reporting, still creating a request-response relationship to power users and IT-experts. The major challenges, limiting casual users from being self-reliant, are: not sufficiently user-friendly tools, poor data definition and lack of data knowledge, limited data access, indigent validation of reports and lastly, inadequate education.

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Rogge-Solti, Andreas. "Probabilistic Estimation of Unobserved Process Events." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7042/.

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Organizations try to gain competitive advantages, and to increase customer satisfaction. To ensure the quality and efficiency of their business processes, they perform business process management. An important part of process management that happens on the daily operational level is process controlling. A prerequisite of controlling is process monitoring, i.e., keeping track of the performed activities in running process instances. Only by process monitoring can business analysts detect delays and react to deviations from the expected or guaranteed performance of a process instance. To enable monitoring, process events need to be collected from the process environment. When a business process is orchestrated by a process execution engine, monitoring is available for all orchestrated process activities. Many business processes, however, do not lend themselves to automatic orchestration, e.g., because of required freedom of action. This situation is often encountered in hospitals, where most business processes are manually enacted. Hence, in practice it is often inefficient or infeasible to document and monitor every process activity. Additionally, manual process execution and documentation is prone to errors, e.g., documentation of activities can be forgotten. Thus, organizations face the challenge of process events that occur, but are not observed by the monitoring environment. These unobserved process events can serve as basis for operational process decisions, even without exact knowledge of when they happened or when they will happen. An exemplary decision is whether to invest more resources to manage timely completion of a case, anticipating that the process end event will occur too late. This thesis offers means to reason about unobserved process events in a probabilistic way. We address decisive questions of process managers (e.g., "when will the case be finished?", or "when did we perform the activity that we forgot to document?") in this thesis. As main contribution, we introduce an advanced probabilistic model to business process management that is based on a stochastic variant of Petri nets. We present a holistic approach to use the model effectively along the business process lifecycle. Therefore, we provide techniques to discover such models from historical observations, to predict the termination time of processes, and to ensure quality by missing data management. We propose mechanisms to optimize configuration for monitoring and prediction, i.e., to offer guidance in selecting important activities to monitor. An implementation is provided as a proof of concept. For evaluation, we compare the accuracy of the approach with that of state-of-the-art approaches using real process data of a hospital. Additionally, we show its more general applicability in other domains by applying the approach on process data from logistics and finance. Unternehmen versuchen Wettbewerbsvorteile zu gewinnen und die Kundenzufriedenheit zu erhöhen. Um die Qualität und die Effizienz ihrer Prozesse zu gewährleisten, wenden Unternehmen Geschäftsprozessmanagement an. Hierbei spielt die Prozesskontrolle im täglichen Betrieb eine wichtige Rolle. Prozesskontrolle wird durch Prozessmonitoring ermöglicht, d.h. durch die Überwachung des Prozessfortschritts laufender Prozessinstanzen. So können Verzögerungen entdeckt und es kann entsprechend reagiert werden, um Prozesse wie erwartet und termingerecht beenden zu können. Um Prozessmonitoring zu ermöglichen, müssen prozessrelevante Ereignisse aus der Prozessumgebung gesammelt und ausgewertet werden. Sofern eine Prozessausführungsengine die Orchestrierung von Geschäftsprozessen übernimmt, kann jede Prozessaktivität überwacht werden. Aber viele Geschäftsprozesse eignen sich nicht für automatisierte Orchestrierung, da sie z.B. besonders viel Handlungsfreiheit erfordern. Dies ist in Krankenhäusern der Fall, in denen Geschäftsprozesse oft manuell durchgeführt werden. Daher ist es meist umständlich oder unmöglich, jeden Prozessfortschritt zu erfassen. Zudem ist händische Prozessausführung und -dokumentation fehleranfällig, so wird z.B. manchmal vergessen zu dokumentieren. Eine Herausforderung für Unternehmen ist, dass manche Prozessereignisse nicht im Prozessmonitoring erfasst werden. Solch unbeobachtete Prozessereignisse können jedoch als Entscheidungsgrundlage dienen, selbst wenn kein exaktes Wissen über den Zeitpunkt ihres Auftretens vorliegt. Zum Beispiel ist bei der Prozesskontrolle zu entscheiden, ob zusätzliche Ressourcen eingesetzt werden sollen, wenn eine Verspätung angenommen wird. Diese Arbeit stellt einen probabilistischen Ansatz für den Umgang mit unbeobachteten Prozessereignissen vor. Dabei werden entscheidende Fragen von Prozessmanagern beantwortet (z.B. "Wann werden wir den Fall beenden?", oder "Wann wurde die Aktivität ausgeführt, die nicht dokumentiert wurde?"). Der Hauptbeitrag der Arbeit ist die Einführung eines erweiterten probabilistischen Modells ins Geschäftsprozessmanagement, das auf stochastischen Petri Netzen basiert. Dabei wird ein ganzheitlicher Ansatz zur Unterstützung der einzelnen Phasen des Geschäftsprozesslebenszyklus verfolgt. Es werden Techniken zum Lernen des probabilistischen Modells, zum Vorhersagen des Zeitpunkts des Prozessendes, zum Qualitätsmanagement von Dokumentationen durch Erkennung fehlender Einträge, und zur Optimierung von Monitoringkonfigurationen bereitgestellt. Letztere dient zur Auswahl von relevanten Stellen im Prozess, die beobachtet werden sollten. Diese Techniken wurden in einer quelloffenen prototypischen Anwendung implementiert. Zur Evaluierung wird der Ansatz mit existierenden Alternativen an echten Prozessdaten eines Krankenhauses gemessen. Die generelle Anwendbarkeit in weiteren Domänen wird examplarisch an Prozessdaten aus der Logistik und dem Finanzwesen gezeigt.

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Rojas, Candio Piero Gilmar, and Pasapera Arturo Alonso Villantoy. "Método de evaluación de variables e indicadores para el proceso de Bloque de Cirugía utilizando Process Mining y Data Visualization." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/653654.

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El presente trabajo consiste en proponer un método que permita formular y evaluar indicadores de Process Mining a través de preguntas relacionadas al funcionamiento de un proceso y permita comprender de manera sencilla las variables del proceso a través de técnicas de Data Visualization. Esta propuesta identifica cuellos de botella y violaciones de políticas de un proceso crítico en una organización de salud, ya que resulta complicado realizar mediciones y análisis para mejorar la calidad y transformación de los procesos en instituciones de atención en el sector salud. Este resultado contribuye a la mejora y optimización de la toma de decisiones por parte del equipo médico del Bloque de Cirugía. Este método está conformado por ocho actividades: 1. Definición de objetivos y preguntas, 2. Extracción de datos, 3. Preprocesamiento de datos, 4. Inspección de registro y patrón, 5. Análisis de Minería de Procesos, 6. Técnicas de Visualización de Datos, 7. Evaluación de resultados y 8. Propuestas de mejora de procesos. In the present work, we proposed a method that allows us to formulate and evaluate Process Mining indicators through questions related to the process traceability, and to bring about a clear understanding of the process variables through Data Visualization techniques. This proposal identifies bottlenecks and violations of policies that arise due to the difficulty of carrying out measurements and analysis for the improvement of process quality assurance and process transformation. The result contributes to the optimization of decision-making by the medical staff involved in the Surgery Block process. This method is divided into eight fundamental activities: 1. Objectives and question definition, 2. Data extraction, 3. Data preprocessing, 4. Registration and pattern inspection, 5. Process mining analysis, 6. Data visualization techniques, 7. Outcome evaluation, and 8. Process improvement approaches. Trabajo de investigación

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Namaki, Araghi Sina. "A methodology for business process discovery and diagnosis based on indoor location data : Application to patient pathways improvement." Thesis, Ecole nationale des Mines d'Albi-Carmaux, 2019. http://www.theses.fr/2019EMAC0014.

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Dans chaque organisation, les processus métier sont aujourd’hui incontournables. Cette thèse vise à développer une méthode pour les améliorer. Dans le domaine de la santé, les organisations hospitalières déploient beaucoup d’efforts pour mettre leurs processus sous contrôle, notamment à cause de la très faible marge d’erreur admise. Les parcours des patients au sein des structures de santé constituent l’application qui a été choisie pour démontrer les apports de cette méthode. Elle a pour originalité d’exploiter les données de géolocalisation des patients à l’intérieur de ces structures. Baptisée DIAG, elle améliore les parcours de soins grâce à plusieurs sous-fonctions : (i) interpréter les données de géolocalisation pour la modélisation de processus, (ii) découvrir automatiquement les processus métier, (iii) évaluer la qualité et la performance des parcours et (iv) diagnostiquer automatiquement les problèmes de performance des processus. Cette thèse propose donc les contributions suivantes : la méthode DIAG elle-même qui, grâce à quatre différents états, extrait les informations des données de géolocalisation ; le méta-modèle DIAG qui a deux utilités : d’une part, interpréter les données de géolocalisation et donc passer des données brutes aux informations utilisables, et, d’autre part contribuer à vérifier l’alignement des données avec le domaine grâce à deux méthodes de diagnostic décrites plus bas ; deux algorithmes de découverte de processus qui utilisent la stabilité statistique des logs d’évènements ; une nouvelle approche de process mining utilisant SPC (Statistical Process Control) pour l’amélioration ; l’algorithme proDIST qui mesure les distances entre les modèles de processus ; deux méthodes de diagnostic automatique de processus pour détecter les causes des déviations structurelles dans des cas individuels et pour des processus communs. Le contexte de cette thèse confirme la nécessité de proposer de telles solutions. Une étude de cas dans le cadre de ce travail de recherche illustre l’applicabilité de la méthodologie DIAG et des fonctions et méthodes mentionnées Business processes are everywhere and, as such, we must acknowledge them. Among all of them, hospital processes are of vital importance. Healthcare organizations invest huge amount of efforts into keeping these processes under control, as the allowed margin of error is so slight. This research work seeks to develop a methodology to endorse improvement of patient pathways inside healthcare organizations. It does so by using the indoor location data of patients. This methodology is called DIAG (Data state, Information state, Awareness, Governance). It is constructed of several different functions. The most important ones are as follows: (i) location data interpreting, (ii) automatic discovery of business process models, (iii) business process analyzing for evaluating the performance and quality of processes, and finally, (iv) automatic diagnosing of business processes. Along the former functions, the contribution of this thesis are: The DIAG methodology which, through four different states, extracts knowledge from location data; the DIAG meta-model which supports both the interpretation of location data (from raw data to usable information) and the alignment of the domain knowledge (which are used for the diagnosing methods); two process discovery algorithms which explore statistical stability in event logs, application of Statistical Process Control (SPC) for the “enhancement notation” of Process Mining; the ProDIST algorithm for measuring the distance between process models; two automatic process diagnosing methods to detect causes of structural deviations in individual cases and common processes. The state of the art in this dissertation endorses the necessity for proposing such solutions. A case study within this research work illustrates the applicability of the DIAG methodology and its mentioned functions and methods

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Assy, Nour. "Automated support of the variability in configurable process models." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLL001/document.

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L'évolution rapide dans les environnements métier d'aujourd'hui impose de nouveaux défis pour la gestion efficace et rentable des processus métiers. Dans un tel environnement très dynamique, la conception des processus métiers devient une tâche fastidieuse, source d'erreurs et coûteuse. Par conséquent, l'adoption d'une approche permettant la réutilisation et l'adaptabilité devient un besoin urgent pour une conception de processus prospère. Les modèles de processus configurables récemment introduits représentent l'une des solutions recherchées permettant une conception de processus par la réutilisation, tout en offrant la flexibilité. Un modèle de processus configurable est un modèle générique qui intègre de multiples variantes de procédés d'un même processus métier à travers des points de variation. Ces points de variation sont appelés éléments configurables et permettent de multiples options de conception dans le modèle de processus. Un modèle de processus configurable doit être configuré selon une exigence spécifique en sélectionnant une option de conception pour chaque élément configurable.Les activités de recherche récentes sur les modèles de processus configurables ont conduit à la spécification des langages de modélisation de processus configurables comme par exemple configurable Event-Driven Process Chain (C-EPC) qui étend la notation de l'EPC avec des éléments configurables. Depuis lors, la question de la conception et de la configuration des modèles de processus configurables a été étudiée. D'une part, puisque les modèles de processus configurables ont tendance à être très complexe avec un grand nombre d'éléments configurables, de nombreuses approches automatisées ont été proposées afin d'assister leur conception. Cependant, les approches existantes proposent de recommander des modèles de processus configurables entiers qui sont difficiles à réutiliser, nécessitent un temps complexe de calcul et peuvent confondre le concepteur du processus. D'autre part, les résultats de la recherche sur la conception des modèles de processus configurables ont mis en évidence la nécessité des moyens de soutien pour configurer le processus. Par conséquent, de nombreuses approches ont proposé de construire un système de support de configuration pour aider les utilisateurs finaux à sélectionner les choix de configuration souhaitables en fonction de leurs exigences. Cependant, ces systèmes sont actuellement créés manuellement par des experts du domaine qui est sans aucun doute une tâche fastidieuse et source d'erreurs .Dans cette thèse, nous visons à automatiser le soutien de la variabilité dans les modèles de processus configurables. Notre objectif est double: (i) assister la conception des processus configurables d'une manière à ne pas confondre les concepteurs par des recommandations complexes et (i) assister la création des systèmes de soutien de configuration afin de libérer les analystes de processus de la charge de les construire manuellement. Pour atteindre le premier objectif, nous proposons d'apprendre de l'expérience acquise grâce à la modélisation des processus passés afin d'aider les concepteurs de processus avec des fragments de processus configurables. Les fragments proposés inspirent le concepteur du processus pour compléter la conception du processus en cours. Pour atteindre le deuxième objectif, nous nous rendons compte que les modèles de processus préalablement conçus et configurés contiennent des connaissances implicites et utiles pour la configuration de processus. Par conséquent, nous proposons de bénéficier de l'expérience acquise grâce à la modélisation et à la configuration passées des processus afin d'aider les analystes de processus dans la construction de leurs systèmes de support de configuration Today's fast changing environment imposes new challenges for effective management of business processes. In such a highly dynamic environment, the business process design becomes time-consuming, error-prone, and costly. Therefore, seeking reuse and adaptability is a pressing need for a successful business process design. Configurable reference models recently introduced were a step toward enabling a process design by reuse while providing flexibility. A configurable process model is a generic model that integrates multiple process variants of a same business process in a given domain through variation points. These variation points are referred to as configurable elements and allow for multiple design options in the process model. A configurable process model needs to be configured according to a specific requirement by selecting one design option for each configurable element.Recent research activities on configurable process models have led to the specification of configurable process modeling notations as for example configurable Event-Driven Process Chain (C-EPC) that extends the EPC notation with configurable elements. Since then, the issue of building and configuring configurable process models has been investigated. On the one hand, as configurable process models tend to be very complex with a large number of configurable elements, many automated approaches have been proposed to assist their design. However, existing approaches propose to recommend entire configurable process models which are difficult to reuse, cost much computation time and may confuse the process designer. On the other hand, the research results on configurable process model design highlight the need for means of support to configure the process. Therefore, many approaches proposed to build a configuration support system for assisting end users selecting desirable configuration choices according to their requirements. However, these systems are currently manually created by domain experts which is undoubtedly a time-consuming and error-prone task.In this thesis, we aim at automating the support of the variability in configurable process models. Our objective is twofold: (i) assisting the configurable process design in a fin-grained way using configurable process fragments that are close to the designers interest and (ii) automating the creation of configuration support systems in order to release the process analysts from the burden of manually building them. In order to achieve the first objective, we propose to learn from the experience gained through past process modeling in order to assist the process designers with configurable process fragments. The proposed fragments inspire the process designer to complete the design of the ongoing process. To achieve the second objective, we realize that previously designed and configured process models contain implicit and useful knowledge for process configuration. Therefore, we propose to benefit from the experience gained through past process modeling and configuration in order to assist process analysts building their configuration support systems. Such systems assist end users interactively configuring the process by recommending suitable configuration decisions

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Sadeghianasl, Sareh. "The quality guardian: Improving activity label quality in event logs through gamification." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229543/1/Sareh_Sadeghianasl_Thesis.pdf.

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Data cleaning, the most tedious task of data analysis, can turn into a fun experience when performed through a game. This thesis shows that the use of gamification and crowdsourcing techniques can mitigate the problem of poor quality of process data. The Quality Guardian, a family of gamified systems, is proposed, which exploits the motivational drives of domain experts to engage with the detection and repair of imperfect activity labels in process data. Evaluation of the developed games using real-life data sets and domain experts shows quality improvement as well as a positive user experience.

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Dobias, Ondrej. "Dolování procesů jako služba." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-319183.

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Softwérové a hardvérové aplikácie zaznamenávajú veľké množstvo informácií do protokolov udalostí. Každé dva roky sa množstvo zaznamenaných dát viac než zdvojnásobí. Dolovanie procesov je relatívne mladá disciplína, ktorá sa nachádza na rozmedzí strojového učenia a dolovania dát na jednej strane a modelovania a analýzy procesov na druhej strane. Cieľom dolovania procesov je popísať a analyzovať skutočné procesy extrahovaním znalostí z protokolov udalostí, ktoré sú v dnešných aplikáciách bežne dostupné. Táto práca mieri na spojenie obchodných príležitostí (organizácie bohaté na dáta; dopyt po službách BPM; limitácie na strane tradičnej dodávky BPM služieb) s technickými možnosťammi Dolovania procesov. Cieľom práce je návrh produktu, ktorý bude riešiť potreby zákazníkov a poskytovateľov služieb v oblasti Dolovania procesov lepšie než súčasné riešenie vybranej spoločnosti.

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Nguyen, Ngoc Chan. "Service recommendation for individual and process use." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00789726.

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Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use. The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are labor-intense, error-prone, time-consuming, and may make business analyst confused. In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases

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Assy, Nour. "Automated support of the variability in configurable process models." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLL001.

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L'évolution rapide dans les environnements métier d'aujourd'hui impose de nouveaux défis pour la gestion efficace et rentable des processus métiers. Dans un tel environnement très dynamique, la conception des processus métiers devient une tâche fastidieuse, source d'erreurs et coûteuse. Par conséquent, l'adoption d'une approche permettant la réutilisation et l'adaptabilité devient un besoin urgent pour une conception de processus prospère. Les modèles de processus configurables récemment introduits représentent l'une des solutions recherchées permettant une conception de processus par la réutilisation, tout en offrant la flexibilité. Un modèle de processus configurable est un modèle générique qui intègre de multiples variantes de procédés d'un même processus métier à travers des points de variation. Ces points de variation sont appelés éléments configurables et permettent de multiples options de conception dans le modèle de processus. Un modèle de processus configurable doit être configuré selon une exigence spécifique en sélectionnant une option de conception pour chaque élément configurable.Les activités de recherche récentes sur les modèles de processus configurables ont conduit à la spécification des langages de modélisation de processus configurables comme par exemple configurable Event-Driven Process Chain (C-EPC) qui étend la notation de l'EPC avec des éléments configurables. Depuis lors, la question de la conception et de la configuration des modèles de processus configurables a été étudiée. D'une part, puisque les modèles de processus configurables ont tendance à être très complexe avec un grand nombre d'éléments configurables, de nombreuses approches automatisées ont été proposées afin d'assister leur conception. Cependant, les approches existantes proposent de recommander des modèles de processus configurables entiers qui sont difficiles à réutiliser, nécessitent un temps complexe de calcul et peuvent confondre le concepteur du processus. D'autre part, les résultats de la recherche sur la conception des modèles de processus configurables ont mis en évidence la nécessité des moyens de soutien pour configurer le processus. Par conséquent, de nombreuses approches ont proposé de construire un système de support de configuration pour aider les utilisateurs finaux à sélectionner les choix de configuration souhaitables en fonction de leurs exigences. Cependant, ces systèmes sont actuellement créés manuellement par des experts du domaine qui est sans aucun doute une tâche fastidieuse et source d'erreurs .Dans cette thèse, nous visons à automatiser le soutien de la variabilité dans les modèles de processus configurables. Notre objectif est double: (i) assister la conception des processus configurables d'une manière à ne pas confondre les concepteurs par des recommandations complexes et (i) assister la création des systèmes de soutien de configuration afin de libérer les analystes de processus de la charge de les construire manuellement. Pour atteindre le premier objectif, nous proposons d'apprendre de l'expérience acquise grâce à la modélisation des processus passés afin d'aider les concepteurs de processus avec des fragments de processus configurables. Les fragments proposés inspirent le concepteur du processus pour compléter la conception du processus en cours. Pour atteindre le deuxième objectif, nous nous rendons compte que les modèles de processus préalablement conçus et configurés contiennent des connaissances implicites et utiles pour la configuration de processus. Par conséquent, nous proposons de bénéficier de l'expérience acquise grâce à la modélisation et à la configuration passées des processus afin d'aider les analystes de processus dans la construction de leurs systèmes de support de configuration Today's fast changing environment imposes new challenges for effective management of business processes. In such a highly dynamic environment, the business process design becomes time-consuming, error-prone, and costly. Therefore, seeking reuse and adaptability is a pressing need for a successful business process design. Configurable reference models recently introduced were a step toward enabling a process design by reuse while providing flexibility. A configurable process model is a generic model that integrates multiple process variants of a same business process in a given domain through variation points. These variation points are referred to as configurable elements and allow for multiple design options in the process model. A configurable process model needs to be configured according to a specific requirement by selecting one design option for each configurable element.Recent research activities on configurable process models have led to the specification of configurable process modeling notations as for example configurable Event-Driven Process Chain (C-EPC) that extends the EPC notation with configurable elements. Since then, the issue of building and configuring configurable process models has been investigated. On the one hand, as configurable process models tend to be very complex with a large number of configurable elements, many automated approaches have been proposed to assist their design. However, existing approaches propose to recommend entire configurable process models which are difficult to reuse, cost much computation time and may confuse the process designer. On the other hand, the research results on configurable process model design highlight the need for means of support to configure the process. Therefore, many approaches proposed to build a configuration support system for assisting end users selecting desirable configuration choices according to their requirements. However, these systems are currently manually created by domain experts which is undoubtedly a time-consuming and error-prone task.In this thesis, we aim at automating the support of the variability in configurable process models. Our objective is twofold: (i) assisting the configurable process design in a fin-grained way using configurable process fragments that are close to the designers interest and (ii) automating the creation of configuration support systems in order to release the process analysts from the burden of manually building them. In order to achieve the first objective, we propose to learn from the experience gained through past process modeling in order to assist the process designers with configurable process fragments. The proposed fragments inspire the process designer to complete the design of the ongoing process. To achieve the second objective, we realize that previously designed and configured process models contain implicit and useful knowledge for process configuration. Therefore, we propose to benefit from the experience gained through past process modeling and configuration in order to assist process analysts building their configuration support systems. Such systems assist end users interactively configuring the process by recommending suitable configuration decisions

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Dissertations / Theses on the topic 'Business Process Mining'

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