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Auswahl der wissenschaftlichen Literatur zum Thema „Business Process Mining“
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Zeitschriftenartikel zum Thema "Business Process Mining"
1
Pourmasoumi, Asef, und Ebrahim Bagheri. „Business process mining“. Encyclopedia with Semantic Computing and Robotic Intelligence 01, Nr. 01 (März 2017): 1630004. http://dx.doi.org/10.1142/s2425038416300044.
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One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.
2
van der Aalst, Wil. „Spreadsheets for business process management“. Business Process Management Journal 24, Nr. 1 (02.02.2018): 105–27. http://dx.doi.org/10.1108/bpmj-10-2016-0190.
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Purpose Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers. Design/methodology/approach The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Findings Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given. Practical implications Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations. Originality/value The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.
3
Ingvaldsen, Jon Espen, und Jon Atle Gulla. „Model-Based Business Process Mining“. Information Systems Management 23, Nr. 1 (Dezember 2006): 19–31. http://dx.doi.org/10.1201/1078.10580530/45769.23.1.20061201/91769.3.
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Polpinij, Jantima, Aditya Ghose und Hoa Khanh Dam. „Mining business rules from business process model repositories“. Business Process Management Journal 21, Nr. 4 (06.07.2015): 820–36. http://dx.doi.org/10.1108/bpmj-01-2014-0004.
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Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.
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Vasiliev, A. A., und A. V. Goryachev. „Applying Process Mining to Process Management“. LETI Transactions on Electrical Engineering & Computer Science 16, Nr. 3 (2023): 52–59. http://dx.doi.org/10.32603/2071-8985-2023-16-3-52-59.
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Deals with the intellectual analysis of processes (Process Mining), which has recently gained popularity in various organizations. It is based on the construction of business process models in a specific area (for example, in the field of project management) based on event logs, providing a more accurate understanding of the actions occurring in business processes for the purpose of their subsequent analysis and improvement. The article defines process mining, event logs, lists the main tasks, algorithms and view models. The authors propose a methodology that can be used in the application of process analysis in the field of project management. The authors also highlight the main business processes in project management, for which it is advisable to build models and analyze them.
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van der Aalst, W. M. P., H. A. Reijers, A. J. M. M. Weijters, B. F. van Dongen, A. K. Alves de Medeiros, M. Song und H. M. W. Verbeek. „Business process mining: An industrial application“. Information Systems 32, Nr. 5 (Juli 2007): 713–32. http://dx.doi.org/10.1016/j.is.2006.05.003.
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7
Badakhshan, Peyman, Bastian Wurm, Thomas Grisold, Jerome Geyer-Klingeberg, Jan Mendling und Jan vom Brocke. „Creating business value with process mining“. Journal of Strategic Information Systems 31, Nr. 4 (Dezember 2022): 101745. http://dx.doi.org/10.1016/j.jsis.2022.101745.
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Er, Mahendrawathi, Hanim Maria Astuti und Dita Pramitasari. „Modeling and Analysis of Incoming Raw Materials Business Process: A Process Mining Approach“. International Journal of Computer and Communication Engineering 4, Nr. 3 (2015): 196–203. http://dx.doi.org/10.17706/ijcce.2015.4.3.196-203.
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9
Park, Sungbum, und Young Sik Kang. „A Study of Process Mining-based Business Process Innovation“. Procedia Computer Science 91 (2016): 734–43. http://dx.doi.org/10.1016/j.procs.2016.07.066.
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Chubukova, Ponomarenko und Nedbailo. „Using data mining to process business data“. Problems of Innovation and Investment Development, Nr. 23 (10.04.2020): 71–77. http://dx.doi.org/10.33813/2224-1213.23.2020.8.
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The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.
Dissertationen zum Thema "Business Process Mining"
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Nguyen, Hoang H. „Stage-aware business process mining“. Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130602/9/Hoang%20Nguyen%20Thesis.pdf.
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Process mining enables the analysis of event logs to gain actionable insights into an organisation’s operations. However, state-of-the-art process mining techniques do not exploit the natural decomposition characteristics of business processes. “Process stages” are a generic type of business process decomposition prevalent in multiple domains, e.g. the stages of loan processing, the support levels in IT helpdesk, or the clinical stages in patient treatment. This study contributes a novel approach to process mining based on process stages. The approach is grounded on four techniques that allow the mining of process stages, the automated discovery of process models, the mining of process performance and the multi-perspective comparison of process variants. The approach has been implemented in an open-source toolset and evaluated with real-life datasets from different domains.
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Bala, Saimir, Macias Cristina Cabanillas, Andreas Solti, Jan Mendling und Axel Polleres. „Mining Project- Oriented Business Processes“. Springer, Cham, 2015. http://dx.doi.org/10.1007/978-3-319-23063-4_28.
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Large engineering processes need to be monitored in detail
regarding when what was done in order to prove compliance with rules
and regulations. A typical problem of these processes is the lack of con-
trol that a central process engine provides, such that it is difficult to
track the actual course of work even if data is stored in version control
systems (VCS). In this paper, we address this problem by defining a
mining technique that helps to generate models that visualize the work
history as GANTT charts. To this end, we formally define the notion of a
project-oriented business process and a corresponding mining algorithm.
Our evaluation based on a prototypical implementation demonstrates
the benefits in comparison to existing process mining approaches for this
specific class of processes.
3
Turner, Christopher James. „A genetic programming based business process mining approach“. Thesis, Cranfield University, 2009. http://dspace.lib.cranfield.ac.uk/handle/1826/4471.
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As business processes become ever more complex there is a need for companies to understand the processes they already have in place. To undertake this manually would be time consuming. The practice of process mining attempts to automatically construct the correct representation of a process based on a set of process execution logs. The aim of this research is to develop a genetic programming based approach for business process mining. The focus of this research is on automated/semi automated business processes within the service industry (by semi automated it is meant that part of the process is manual and likely to be paper based). This is the first time a GP approach has been used in the practice of process mining. The graph based representation and fitness parsing used are also unique to the GP approach. A literature review and an industry survey have been undertaken as part of this research to establish the state-of-the-art in the research and practice of business process modelling and mining. It is observed that process execution logs exist in most service sector companies are not utilised for process mining. The development of a new GP approach is documented along with a set of modifications required to enable accuracy in the mining of complex process constructs, semantics and noisy process execution logs. In the context of process mining accuracy refers to the ability of the mined model to reflect the contents of the event log on which it is based; neither over describing, including features that are not recorded in the log, or under describing, just including the most common features leaving out low frequency task edges, the contents of the event log. The complexity of processes, in terms of this thesis, involves the mining of parallel constructs, processes containing complex semantic constructs (And/XOR split and join points) and processes containing 20 or more tasks. The level of noise mined by the business process mining approach includes event logs which have a small number of randomly selected tasks missing from a third of their structure. A novel graph representation for use with GP in the mining of business processes is presented along with a new way of parsing graph based individuals against process execution logs. The GP process mining approach has been validated with a range of tests drawn from literature and two case studies, provided by the industrial sponsor, utilising live process data. These tests and case studies provide a range of process constructs to fully test and stretch the GP process mining approach. An outlook is given into the future development of the GP process mining approach and process mining as a practice.
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Burattin, Andrea <1984>. „Applicability of Process Mining Techniques in Business Environments“. Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5446/1/thesis-final-v4.pdf.
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This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques.
The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity.
We concentrate on two possible scenarios: "batch" and "on-line" Process Mining.
Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated.
After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance.
Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models.
Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives.
The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).
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Burattin, Andrea <1984>. „Applicability of Process Mining Techniques in Business Environments“. Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5446/.
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This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques.
The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity.
We concentrate on two possible scenarios: "batch" and "on-line" Process Mining.
Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated.
After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance.
Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models.
Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives.
The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).
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Al, Jlailaty Diana. „Mining Business Process Information from Emails Logs for Process Models Discovery“. Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED028.
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Les informations échangées dans les textes des courriels sont généralement concernées par des événements complexes ou des processus métier dans lesquels les entités qui échangent des courriels collaborent pour atteindre les objectifs finaux des processus. Ainsi, le flux d’informations dans les courriels envoyés et reçus constitue une partie essentielle, les activités métier de l’entreprise. L’extraction d’informations sur les processus métier à partir des courriels peut aider à améliorer la gestion des courriels pour les utilisateurs. Il peut également être utilisé pour trouver des réponses riches à plusieurs questions analytiques sur les employés et les organisations. Aucun des travaux précédents n’a résolu le problème de la transformation automatique des journaux de courriels en journaux d’événements pour éventuellement en déduire les processus métier non documentés. Dans ce but, nous travaillons dans cette thèse sur un framework qui induit des informations de processus métier à partir d’emails. Nous introduisons des approches qui contribuent à ce qui suit : (1) découvrir pour chaque courriel le sujet de processus qui le concerne, (2) découvrir l’instance de processus métier à laquelle appartient chaque courriel, (3) extraire les activités de processus métier des courriels et associer ces activités aux métadonnées qui les décrivent, (4) améliorer la performance de la découverte des instances de processus métier et des activités métier en utilisant la relation entre ces deux problèmes, et enfin (5) estimer au préalable la date/heure réelle d’un activité métier. En utilisant les résultats des approches mentionnées, un journal d’événements est généré qui peut être utilisé pour déduire les modèles de processus métier d’un journal de courriels. L’efficacité de toutes les approches ci-dessus est prouvée par l’application de plusieurs expériences sur l’ensemble de données de courriel ouvert d’EnronExchanged information in emails’ texts is usually concerned by complex events or business processes in which the entities exchanging emails are collaborating to achieve the processes’ final goals. Thus, the flow of information in the sent and received emails constitutes an essential part of such processes i.e. the tasks or the business activities. Extracting information about business processes from emails can help in enhancing the email management for users. It can be also used in finding rich answers for several analytical queries about the employees and the organizations enacting these business processes. None of the previous works have fully dealt with the problem of automatically transforming email logs into event logs to eventually deduce the undocumented business processes. Towards this aim, we work in this thesis on a framework that induces business process information from emails. We introduce approaches that contribute in the following: (1) discovering for each email the process topic it is concerned by, (2) finding out the business process instance that each email belongs to, (3) extracting business process activities from emails and associating these activities with metadata describing them, (4) improving the performance of business process instances discovery and business activities discovery from emails by making use of the relation between these two problems, and finally (5) preliminary estimating the real timestamp of a business process activity instead of using the email timestamp. Using the results of the mentioned approaches, an event log is generated which can be used for deducing the business process models of an email log. The efficiency of all of the above approaches is proven by applying several experiments on the open Enron email dataset
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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 réelsMore 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 réelsMore 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 futuresBusiness 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
Bücher zum Thema "Business Process Mining"
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Burattin, Andrea. Process Mining Techniques in Business Environments. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17482-2.
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service), SpringerLink (Online, Hrsg. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
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Piattini, Mario, und Ricardo Perez-Castillo. Uncovering essential software artifacts through business process archeology. Hershey: Business Science Reference, an imprint of IGI Global, 2014.
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Buchwald, Hagen. S-BPM ONE – Setting the Stage for Subject-Oriented Business Process Management: First International Workshop, Karlsruhe, Germany, October 22, 2009. Revised Selected Papers. Berlin, Heidelberg: Springer-Verlag Heidelberg, 2010.
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Kumar, Akhil. Business Process Management. Taylor & Francis Group, 2018.
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Business Process Management. Routledge, 2018.
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Business Process Management. Taylor & Francis Group, 2018.
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Kumar, Akhil. Business Process Management. Taylor & Francis Group, 2018.
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Burattin, Andrea. Process Mining Techniques in Business Environments: Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining. Springer, 2015.
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Burattin, Andrea. Process Mining Techniques in Business Environments: Theoretical Aspects, Algorithms, Techniques and Open Challenges in Process Mining. Springer, 2015.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Business Process Mining"
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Burattin, Andrea. „Process Mining“. In Process Mining Techniques in Business Environments, 33–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17482-2_5.
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Leemans, Sander J. J. „Process Mining“. In Lecture Notes in Business Information Processing, 49–117. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96655-3_3.
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van der Aalst, Wil, Arya Adriansyah, Ana Karla Alves de Medeiros, Franco Arcieri, Thomas Baier, Tobias Blickle, Jagadeesh Chandra Bose et al. „Process Mining Manifesto“. In Business Process Management Workshops, 169–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28108-2_19.
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Folino, Francesco, und Luigi Pontieri. „Business Process Deviance Mining“. In Encyclopedia of Big Data Technologies, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_100-1.
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Folino, Francesco, und Luigi Pontieri. „Business Process Deviance Mining“. In Encyclopedia of Big Data Technologies, 389–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_100.
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Buffett, Scott, und Bruce Hamilton. „Abductive Workflow Mining“. In Business Process Management Workshops, 158–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00328-8_15.
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Syed, Rehan, Sander J. J. Leemans, Rebekah Eden und Joos A. C. M. Buijs. „Process Mining Adoption“. In Lecture Notes in Business Information Processing, 229–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58638-6_14.
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Mannhardt, Felix. „Responsible Process Mining“. In Lecture Notes in Business Information Processing, 373–401. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08848-3_12.
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AbstractThe prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to considering the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white-box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. This chapter structures the topic of responsible process mining based on the FACT criteria: Fairness, Accuracy, Confidentiality, and Transparency. For each criteria challenges specific to process mining are provided and the current state of the art is briefly summarized.
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Dumas, Marlon, Marcello La Rosa, Volodymyr Leno, Artem Polyvyanyy und Fabrizio Maria Maggi. „Robotic Process Mining“. In Lecture Notes in Business Information Processing, 468–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08848-3_16.
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AbstractUser interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For example, by analyzing such logs, we can identify best practices for executing a given task in a process, or we can elicit differences in performance between workers or between teams. Furthermore, user interaction logs allow us to discover repetitive and automatable routines that occur during the execution of one or more tasks in a process. Along this line, this chapter introduces a family of techniques, called Robotic Process Mining (RPM), which allow us to discover repetitive routines that can be automated using robotic process automation technology. The chapter presents a structured landscape of concepts and techniques for RPM, including techniques for user interaction log preprocessing, techniques for discovering frequent routines, notions of routine automatability, as well as techniques for synthesizing executable routine specifications for robotic process automation.
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Burattin, Andrea. „Streaming Process Mining“. In Lecture Notes in Business Information Processing, 349–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08848-3_11.
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AbstractStreaming process mining refers to the set of techniques and tools which have the goal of processing a stream of data (as opposed to a finite event log). The goal of these techniques, similarly to their corresponding counterparts described in the previous chapters, is to extract relevant information concerning the running processes. This chapter presents an overview of the problems related to the processing of streams, as well as a categorization of the existing solutions. Details about control-flow discovery and conformance checking techniques are also presented together with a brief overview of the state of the art.
Konferenzberichte zum Thema "Business Process Mining"
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López-Pintado, Orlenys, Serhii Murashko und Marlon Dumas. „Discovery and Simulation of Data-Aware Business Processes“. In 2024 6th International Conference on Process Mining (ICPM), 105–12. IEEE, 2024. http://dx.doi.org/10.1109/icpm63005.2024.10680675.
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Kirchdorfer, Lukas, Robert Blümel, Timotheus Kampik, Han Van der Aa und Heiner Stuckenschmidt. „AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation“. In 2024 6th International Conference on Process Mining (ICPM), 97–104. IEEE, 2024. http://dx.doi.org/10.1109/icpm63005.2024.10680660.
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Pasquadibisceglie, Vincenzo, Annalisa Appice und Donato Malerba. „LUPIN: A LLM Approach for Activity Suffix Prediction in Business Process Event Logs“. In 2024 6th International Conference on Process Mining (ICPM), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/icpm63005.2024.10680620.
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Wuyts, Brecht, Seppe Vanden Broucke und Jochen De Weerdt. „SuTraN: an Encoder-Decoder Transformer for Full-Context-Aware Suffix Prediction of Business Processes“. In 2024 6th International Conference on Process Mining (ICPM), 17–24. IEEE, 2024. http://dx.doi.org/10.1109/icpm63005.2024.10680671.
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Zaidi, Taskeen, Shivam Khurana, Kunal Sharma, S. Jayasree, Rupali A. Mahajan und Megha Pandey. „Evaluating the Usefulness of Data Mining for Business Process Automation“. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723900.
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Chinces, Diana, und Ioan Salomie. „Business process mining algorithms“. In 2013 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2013. http://dx.doi.org/10.1109/iccp.2013.6646120.
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Esfahani, Faramarz Safi, Masrah Azrifah Azmi Murad, Md Nasir Sulaiman und Nur Izura Udzir. „Using process mining to business process distribution“. In the 2009 ACM symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1529282.1529755.
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Lautenbacher, Florian, Bernhard Bauer und Sebastian Forg. „Process mining for semantic business process modeling“. In 2009 13th Enterprise Distributed Object Computing Conference Workshops, EDOCW. IEEE, 2009. http://dx.doi.org/10.1109/edocw.2009.5332017.
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Djedovic, Almir, Emir Zunic und Almir Karabegovic. „A combined process mining for improving business process“. In 2017 International Conference on Smart Systems and Technologies (SST). IEEE, 2017. http://dx.doi.org/10.1109/sst.2017.8188685.
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Tang Hongtao, Chen Yong und Lu Jiansa. „Architecture of process mining based business process optimization“. In International Technology and Innovation Conference 2006 (ITIC 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060919.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Business Process Mining"
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Бакум, З. П., und В. В. Ткачук. Mining Engineers Training in Context of Innovative System of Ukraine. Криворізький державний педагогічний університет, 2014. http://dx.doi.org/10.31812/0564/425.
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The paper clarifies the process of a mining engineer training in terms of the innovation system of Ukraine. The authors analyze a number of normative documents concerning innovative activity in Ukraine in general and mining business in particular. In the process of implementation of innovations into mining industrial complex urgent problems are defined. The methodology of information and communication technologies (electronic, distance and mobile studies) usage in engineers training within the conditions of university education is offered. It is marked that the worked out methodology finds its practical introduction: e-learning involves creation of the portal "Electronic mentor"; distance learning is presented in the study of professional disciplines as an example of the course "Сomputer Technologies in Mining"; mobile learning is considered as an example of discipline "Computer Science and Engineering".
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Volkova, Nataliia P., Nina O. Rizun und Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.
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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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Bernal, Richard L. Chinese Foreign Direct Investment in the Caribbean: Potential and Prospects. Inter-American Development Bank, November 2016. http://dx.doi.org/10.18235/0009313.
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China has recently become the third largest source of foreign direct investment (FDI) in the world and is a major source of development aid for developing countries, including those in the Caribbean. The capital flows it provides have taken the form of loans to governments to finance infrastructure projects and to expand production of oil and other raw materials. There have been indications of interest in further investment in the Caribbean from Chinese enterprises and entrepreneurs. This is driven by both state policy, which seeks to more actively integrate China into the global economy, and enterprise-level interests by both state and private enterprises. Caribbean governments have generally been receptive to prospective incoming FDI. This paper identifies the instances of Chinese FDI in the Caribbean to date and discusses the potential for further investment, highlighting opportunities in agriculture, tourism, manufacturing, mining, infrastructure, and fishing. The factors influencing the emergence of Chinese FDI in the Caribbean include: the ongoing learning process by both investors and the governments of host countries; the performance of vanguard companies; transnational ethnic business networks; the local business environment; and the policies of Caribbean governments.
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Price, Roz. Taxation and Public Financial Management of Mining Revenue in the Democratic Republic of Congo. Institute of Development Studies (IDS), Oktober 2021. http://dx.doi.org/10.19088/k4d.2021.144.
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This rapid review provides a summary of the evidence on the taxation and public financial management of mining revenues in the Democratic Republic of Congo (DRC). This is a very complex topic, with a large and growing literature base, a huge interest by donors, non-governmental organisations and businesses, with some conflicting information at times. In particular, specific data on provincial budgets and spending was not identified during this review. No specific information on public financial management in either of these provinces was identified during the course of this review. Given the burgeoning size of the literature base and the complexity of the mining sector in the DRC, this rapid review only provides a snapshot of the literature. It draws on academic, grey and donor literature sources. Some papers for further reading are highlighted. The report first provides a brief background discussion of general taxation in the DRC, the decentralisation process, and provincial public revenue management. The next section provides general information on the mining sector in the DRC, including the regulatory system and official duties, royalties and tax provisions. Section 4 goes into more detail about taxation and rent-seeking in the mining sector, touching on both large-scale mining (LSM) and artisanal and small-scale mining (ASM). The next section looks at smuggling of minerals in the DRC, with a focus on gold. Finally, some specific lessons learned were drawn from two World Bank projects and highlighted in the final section. Lessons and experiences from other mining-related projects are also highlighted throughout the report. Literature in French was not included in this rapid review, which may mean that some key documents were omitted.
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Kornelakis, Andreas, Chiara Benassi, Damian Grimshaw und Marcela Miozzo. Robots at the Gates? Robotic Process Automation, Skills and Institutions in Knowledge-Intensive Business Services. Digital Futures at Work Research Centre, Mai 2022. http://dx.doi.org/10.20919/vunu3389.
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Against the backdrop of the fourth industrial revolution, this paper examines the emergence of Robotic Process Automation (RPA) as one of the new technologies that are shaping the future of work and reconfiguring sectoral business and innovation systems and models. It discusses how the institutional context can potentially mediate the digital transformation of services, how RPA affects workers’ employment and skills, and how it alters inter-organisational relationships and capabilities. Bringing together different strands of academic literature on employment studies, innovation, and technology studies, it deploys a comparative institutional perspective to explore the potential effects of RPA and illustrates their plausibility through mini case studies from knowledge-intensive business services
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Pueyo, Ana, Gisela Ngoo, Editruda Daulinge und Adriana Fajardo. The Quest for Scalable Business Models for Mini-Grids in Africa: Implementing the Keymaker Model in Tanzania. Institute of Development Studies, Oktober 2022. http://dx.doi.org/10.19088/ids.2022.071.
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Achieving universal electrification in sub-Saharan Africa requires creative solutions. Renewable mini-grids are a promising technology to electrify remote communities with a substantial productive demand, mainly from agro-processing. Mini-grids have experienced fast growth and there are now around 2,200 systems in the sub-Saharan Africa region. However, their economic case in the sub-continent is unclear. Most mini-grids are struggling not only to obtain a profit but also to recover costs. This Research Report describes the case of a private company in Tanzania implementing a business model for mini-grids that promotes productive uses of energy to achieve financial sustainability (the ‘Keymaker model’). A group of researchers worked jointly with the mini-grid developer to procure equipment for fish processing activities, support local entrepreneurs to use electricity productively, and to document and learn from the process. Although the business model was ultimately unsuccessful – facing high regulatory risks, high initial tariffs required to recover costs, and complex management of agro-processing activities – the project offers useful lessons and considerations for future efforts to promote mini-grids, and how public–private partnerships can help improve affordability and reduce regulatory risks.
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Olsson, Olle. Industrial decarbonization done right: identifying success factors for well-functioning permitting processes. Stockholm Environment Institute, November 2021. http://dx.doi.org/10.51414/sei2021.034.
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1 Introduction 1.1 The urgency of industrial decarbonization The last few years have seen several of the world’s largest carbon dioxide-emitting countries and leading heavy industry companies committing to mid-century net-zero targets (Buckley 2021; Denyer and Kashiwagi 2020; McCurry 2020; Myers 2020). Consequently, the discussion on economy-wide transition to net-zero is accelerating, with focus shifting from “if” to “when” and “how”, even for heavy industry sectors like steel, cement and chemicals. This makes it increasingly urgent to analyse not just whether it is technologically feasible to decarbonize heavy industry, but also investigate issues more directly related to practical implementation. This includes site-specific planning, infrastructure availability, and consultation with local authorities and other stakeholders. Many of the latter considerations are formalized as part of the permitting processes that are an essential vehicle to ensure that industrial interests are balanced against interests of society at large. However, doing this balancing act can turn out to be very complicated and associated with uncertainties as to their outcome, as well as being demanding in resources and time. At the same time, to ensure broad buy-in and support from society, the investments needed must be implemented in a way that takes a broad spectrum of sustainability concerns into account, not just climate change mitigation. A key question is if and how permitting processes can run more smoothly and efficiently while still ensuring inclusive consultations, fair procedures and adherence to legal certainty. This policy brief discusses this question from the starting point of Swedish conditions, but many of the points raised will be relevant for a broader international discussion on taking industrial decarbonization to implementation. 1.2 Industrial transition and permitting processes in Sweden Decarbonization of the industrial sector in Sweden essentially entails a relatively small number of investment projects in the cement, steel, petrochemical and refinery sectors, where the vast majority of carbon emissions are concentrated (Karltorp et al. 2019; Nykvist et al. 2020). However, while few in number, the size of these investments means that their implementation will by necessity become relevant to many other parts of society. In connection with the increasing focus on how to implement industrial decarbonization in Sweden, discussions about permitting processes have been brought higher up on the agenda. While there has been an active discussion on permitting processes in Sweden for quite some time, it has primarily been focused on aspects related to mining and wind power (Larsen et al. 2017; Raitio et al. 2020). The last few years have, however, focused increasingly on industrial projects, in particular related to a proposed – though eventually cancelled – expansion of an oil refinery in the southwestern part of the country (Blad 2020). In terms of political discussions, both the governmental initiative Fossil-free Sweden (2020) and the Swedish Climate Policy Council (2020) emphasize that permitting processes need to become faster in order for Sweden’s industrial transition to be implemented in line with the time plan set by the 2017 Swedish Climate Act. Business representatives and organizations are also voicing concerns about the slow speed of permitting (Balanskommissionen 2019; Jacke 2018). At the same time, criticism has been raised that much of the environmental damage done in Sweden comes from activities conducted within limits set by environmental permits, which could be a flaw in the system (Malmaeus and Lindblom 2019). Finally, recent public inquiries have also discussed permitting processes.
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Hutchinson, M. L., J. E. L. Corry und R. H. Madden. A review of the impact of food processing on antimicrobial-resistant bacteria in secondary processed meats and meat products. Food Standards Agency, Oktober 2020. http://dx.doi.org/10.46756/sci.fsa.bxn990.
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For meat and meat products, secondary processes are those that relate to the downstream of the primary chilling of carcasses. Secondary processes include maturation chilling, deboning, portioning, mincing and other operations such as thermal processing (cooking) that create fresh meat, meat preparations and ready-to-eat meat products. This review systematically identified and summarised information relating to antimicrobial resistance (AMR) during the manufacture of secondary processed meatand meat products (SPMMP). Systematic searching of eight literature databases was undertaken and the resultantpapers were appraised for relevance to AMR and SPMMP. Consideration was made that the appraisal scores, undertaken by different reviewers, were consistent. Appraisal reduced the 11,000 initially identified documents to 74, which indicated that literature relating to AMR and SPMMP was not plentiful. A wide range of laboratory methods and breakpoint values (i.e. the concentration of antimicrobial used to assess sensitivity, tolerance or resistance) were used for the isolation of AMR bacteria.The identified papers provided evidence that AMR bacteria could be routinely isolated from SPMMP. There was no evidence that either confirmed or refuted that genetic materials capable of increasing AMR in non-AMR bacteria were present unprotected (i.e. outside of a cell or a capsid) in SPMMP. Statistical analyses were not straightforward because different authors used different laboratory methodologies.However, analyses using antibiotic organised into broadly-related groups indicated that Enterobacteriaceaeresistant to third generation cephalosporins might be an area of upcoming concern in SPMMP. The effective treatment of patients infected with Enterobacteriaceaeresistant to cephalosporins are a known clinical issue. No AMR associations with geography were observed and most of the publications identified tended to be from Europe and the far east.AMR Listeria monocytogenes and lactic acid bacteria could be tolerant to cleaning and disinfection in secondary processing environments. The basis of the tolerance could be genetic (e.g. efflux pumps) or environmental (e.g. biofilm growth). Persistent, plant resident, AMR L. monocytogenes were shown by one study to be the source of final product contamination. 4 AMR genes can be present in bacterial cultures used for the manufacture of fermented SPMMP. Furthermore, there was broad evidence that AMR loci could be transferred during meat fermentation, with refrigeration temperatures curtailing transfer rates. Given the potential for AMR transfer, it may be prudent to advise food business operators (FBOs) to use fermentation starter cultures that are AMR-free or not contained within easily mobilisable genetic elements. Thermal processing was seen to be the only secondary processing stage that served as a critical control point for numbers of AMR bacteria. There were significant linkages between some AMR genes in Salmonella. Quaternary ammonium compound (QAC) resistance genes were associated with copper, tetracycline and sulphonamide resistance by virtue of co-location on the same plasmid. No evidence was found that either supported or refuted that there was any association between AMR genes and genes that encoded an altered stress response or enhanced the survival of AMR bacteria exposed to harmful environmental conditions.