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Relevant bibliographies by topics / Semantic process mining

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Academic literature on the topic 'Semantic process mining'

Author: Grafiati

Published: 21 February 2023

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Journal articles on the topic "Semantic process mining"

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Liao, Yong Xin, Eduardo Rocha Loures, Eduardo Alves Portela Santos, and Osiris Canciglieri. "The Proposition of a Framework for Semantic Process Mining." Advanced Materials Research 1051 (October 2014): 995–99. http://dx.doi.org/10.4028/www.scientific.net/amr.1051.995.

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As one of the hot topics in Business Process Management (BPM), process mining aims at constructing models to explain what is actually happening from different perspectives based on the process-related information that automatically extracted from event logs. Because the semantics of the data that recorded in event logs are not usually explicit, current mining approaches are somewhat limited. A number of studies have been carried out in the combination use of formalized semantic models and process mining technologies to obtain the semantic mining capability. However, among these researches, the

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Okoye, Kingsley. "Semantic process mining: A conceptual application of main tools, framework and model analysis." International Journal of Hybrid Intelligent Systems 16, no. 3 (2020): 127–47. http://dx.doi.org/10.3233/his-200286.

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Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect,

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Ingvaldsen, Jon Espen, and Jon Atle Gulla. "Industrial application of semantic process mining." Enterprise Information Systems 6, no. 2 (2012): 139–63. http://dx.doi.org/10.1080/17517575.2011.593103.

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Jangi, Majid, Fateme Moghbeli, Mahya Ghaffari, and Alireza Vahedinemani. "Hospital Management Based On Semantic Process Mining: A Systematic Review." Frontiers in Health Informatics 8, no. 1 (2019): 4. http://dx.doi.org/10.30699/fhi.v8i1.171.

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Introduction: Semantic Process Mining is the extension field of process mining that is based on getting knowledge of conceptual event logs (based on ontologies) for analyzing frequent and rare processes. In the healthcare studies, semantic process mining has been used in different hospitals in order to improve processes.Material and Methods: A review of the usages of semantic process mining in hospitals is done. This review contains 65 articles from PubMed, dblp and Google scholar. It is searched from 2000 to 2017. One of them was duplicated and finally, we received 64 articles. Data were extr

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Zhang, Jun, Xiangfeng Luo, Xiang He, and Chuanliang Cai. "Text Semantic Mining Model Based on the Algebra of Human Concept Learning." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 2 (2011): 80–96. http://dx.doi.org/10.4018/jcini.2011040105.

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Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model (TSMM) based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra

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Yao, Liguo, Haisong Huang, and Shih-Huan Chen. "Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules." Processes 8, no. 7 (2020): 751. http://dx.doi.org/10.3390/pr8070751.

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Companies accumulate a large amount of production process data during product manufacturing. Sequence data from the mining production process can enable a company to evaluate the manufacturing process, to find the key factors affecting product quality, and to improve product quality. However, the production process mainly exists in the form of text. To solve this problem, we propose a novel frequent pattern mining algorithm (EABMC) based on the text context semantics and rules of the manufacturing process to remove redundant sequences and to obtain good mining results. In this algorithm, first

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Mukasheva, Gulzira, Natalya Stenina, and Kakim Sagindykov. "Assessment of the Protection and Efficiency of Work of Information Systems Used in the Mining Industry." E3S Web of Conferences 105 (2019): 03003. http://dx.doi.org/10.1051/e3sconf/201910503003.

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The issues of digitalization of the mining industry, including open-pit mining, are considered. The issues of the use of semantic systems as an element of information flows, allowing to determine and select the right decisions in the process of mining management, in particular, in the mining of minerals by open mining, are covered. The article formulates requirements for semantic networks for the description of material in computer-aided learning tools and analyzes the most well-known models of semantic networks.

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Alkhanova, Gulnur, Dmitry Stenin, and Serik Zhuzbaev. "The Semantic Network as a Promising Information Platform in the Mining Industry." E3S Web of Conferences 105 (2019): 03015. http://dx.doi.org/10.1051/e3sconf/201910503015.

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The issues of digitalization of the mining industry, including open-pit mining, are considered. The issues of the use of semantic systems as an element of information flows, allowing to determine and select the right decisions in the process of mining management, in particular, in the mining of minerals by open mining, are covered. The article formulates requirements for semantic networks for the description of material in computer-aided learning tools and analyzes the most well-known models of semantic networks.

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Pereira Detro, Silvana, Eduardo Alves Portela Santos, Hervé Panetto, Eduardo De Loures, Mario Lezoche, and Claudia Cabral Moro Barra. "Applying process mining and semantic reasoning for process model customisation in healthcare." Enterprise Information Systems 14, no. 7 (2019): 983–1009. http://dx.doi.org/10.1080/17517575.2019.1632382.

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Traxler, Barbara, Emmanuel Helm, Oliver Krauss, Andreas Schuler, and Josef Kueng. "Towards Semantic Interoperability in Health Data Management Facilitating Process Mining." International Journal of Privacy and Health Information Management 6, no. 2 (2018): 1–12. http://dx.doi.org/10.4018/ijphim.2018070101.

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As an evidence-based business process analysis method, process mining can be used to investigate variations in delivery of care. Existing approaches are only based on one data source. A variety of data sources means different domain languages and understanding, special processes workflows in various organizations, varying documentation with different goals and different designations and varying use of coding systems. This article describes a modular, rule-based information extraction algorithm based on CDA and compares it to a proprietary healthcare reference model approach and a resource-base

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Dissertations / Theses on the topic "Semantic process mining"

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Detro, Silvana Pereira. "A Framework for Managing Process Variability Through Process Mining and Semantic Reasoning : An Application in Healthcare." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0310/document.

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Les organisations doivent relever le défi d'adapter leurs processus aux changements qui peuvent survenir dans l'environnement dynamique dans lequel elles opèrent. Les adaptations dans le processus aboutissent à plusieurs variantes de processus, c'est-à-dire dans différentes versions du modèle de processus. Les variantes de processus peuvent différer en termes d'activités, de ressources, de flux de contrôle et de données. Ainsi, le concept d'un modèle de processus personnalisable est apparu et il vise à adapter le modèle de processus en fonction des exigences d'un contexte spécifique. Un modèle

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Hjelm, Hans. "Cross-language Ontology Learning : Incorporating and Exploiting Cross-language Data in the Ontology Learning Process." Doctoral thesis, Stockholms universitet, Institutionen för lingvistik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8414.

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An ontology is a knowledge-representation structure, where words, terms or concepts are defined by their mutual hierarchical relations. Ontologies are becoming ever more prevalent in the world of natural language processing, where we currently see a tendency towards using semantics for solving a variety of tasks, particularly tasks related to information access. Ontologies, taxonomies and thesauri (all related notions) are also used in various variants by humans, to standardize business transactions or for finding conceptual relations between terms in, e.g., the medical domain. The acquisition

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Striani, Manuel. "A Knowledge-based abstraction framework for trace comparison and semantic process mining." Doctoral thesis, 2019. http://hdl.handle.net/2318/1712735.

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Event logs constitute a rich source of information for several process analysis activities, which can take advantage of similar traces retrieval. The capability of relating semantic structures such as taxonomies to actions in the traces can enable trace comparison to work at different levels of abstraction and, therefore, to mask irrelevant details, and make the identfication of similar traces much more flexible. For this reason, this thesis proposes a trace abstraction mechanism based on domain knowledge, which maps actions in the log traces to instances of ground concepts in an ontology, an

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Books on the topic "Semantic process mining"

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Okoye, Kingsley. Applications and Developments in Semantic Process Mining. IGI Global, 2020.

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Applications and Developments in Semantic Process Mining. Engineering Science Reference, 2020.

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Okoye, Kingsley. Applications and Developments in Semantic Process Mining. IGI Global, 2020.

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Okoye, Kingsley. Applications and Developments in Semantic Process Mining. IGI Global, 2020.

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Okoye, Kingsley. Applications and Developments in Semantic Process Mining. IGI Global, 2020.

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Book chapters on the topic "Semantic process mining"

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Wang, Qi, and Phillip C. y. Sheu. "Process Mining and Description." In Semantic Computing. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470588222.ch10.

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Calvanese, Diego, Tahir Emre Kalayci, Marco Montali, and Ario Santoso. "OBDA for Log Extraction in Process Mining." In Reasoning Web. Semantic Interoperability on the Web. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61033-7_9.

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Dogan, Onur, Ekin Akkol, and Muge Olucoglu. "Understanding Patient Activity Patterns in Smart Homes with Process Mining." In Knowledge Graphs and Semantic Web. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21422-6_22.

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Montani, Stefania, Manuel Striani, Silvana Quaglini, Anna Cavallini, and Giorgio Leonardi. "Knowledge-Based Trace Abstraction for Semantic Process Mining." In Artificial Intelligence in Medicine. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59758-4_30.

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de Medeiros, A. K. Alves, C. Pedrinaci, W. M. P. van der Aalst, et al. "An Outlook on Semantic Business Process Mining and Monitoring." In On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76890-6_52.

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Zhai, Xin-wei, and De-gan Zhang. "A New Service Mining Method Based on Semantic Process." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22418-8_2.

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Leonardi, G., M. Striani, S. Quaglini, A. Cavallini, and S. Montani. "Towards Semantic Process Mining Through Knowledge-Based Trace Abstraction." In Lecture Notes in Business Information Processing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11638-5_3.

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Ly, Linh Thao, Conrad Indiono, Jürgen Mangler, and Stefanie Rinderle-Ma. "Data Transformation and Semantic Log Purging for Process Mining." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-642-31095-9_16.

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Okoye, Kingsley. "Semantic-Based Process Mining: A Conceptual Model Analysis and Framework." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49339-4_20.

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Barbieri, Luciana, Edmundo Roberto Mauro Madeira, Kleber Stroeh, and Wil M. P. van der Aalst. "Towards a Natural Language Conversational Interface for Process Mining." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_20.

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AbstractDespite all the recent advances in process mining, making it accessible to non-technical users remains a challenge. In order to democratize this technology and make process mining ubiquitous, we propose a conversational interface that allows non-technical professionals to retrieve relevant information about their processes and operations by simply asking questions in their own language. In this work, we propose a reference architecture to support a conversational, process mining oriented interface to existing process mining tools. We combine classic natural language processing techniques (such as entity recognition and semantic parsing) with an abstract logical representation for process mining queries. We also provide a compilation of real natural language questions (aiming to form a dataset of that sort) and an implementation of the architecture that interfaces to an existing commercial tool: Everflow. Last but not least, we analyze the performance of this implementation and point out directions for future work.

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Conference papers on the topic "Semantic process mining"

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Lautenbacher, Florian, Bernhard Bauer, and 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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Khannat, Aicha, Hanae Sbai, and Laila Kjiri. "Configurable Process Mining: Semantic Variability in Event Logs." In 23rd International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010484207680775.

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"SEMANTIC PROCESS MINING FOR THE VERIFICATION OF MEDICAL RECOMMENDATIONS." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003112600050016.

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Bhatia, C. S., and Suresh Jain. "Semantic Web Mining: Using Ontology Learning and Grammatical Rule Inference Technique." In 2011 International Conference on Process Automation, Control and Computing (PACC). IEEE, 2011. http://dx.doi.org/10.1109/pacc.2011.5979024.

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Nykänen, Ossi, Alejandro Rivero-Rodriguez, Paolo Pileggi, Pekka A. Ranta, Meri Kailanto, and Juho Koro. "Associating event logs with ontologies for semantic process mining and analysis." In AcademicMindTrek'15: Academic Mindtrek Conference 2015. ACM, 2015. http://dx.doi.org/10.1145/2818187.2818273.

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Okoye, Kingsley, Abdel Rahman H. Tawil, Usman Naeem, and Elyes Lamine. "Semantic Process Mining Towards Discovery and Enhancement of Learning Model Analysis." In 2015 IEEE 17th International Conference on High-Performance Computing and Communications; 2015 IEEE 7th International Symposium on Cyberspace Safety and Security; and 2015 IEEE 12th International Conference on Embedded Software and Systems. IEEE, 2015. http://dx.doi.org/10.1109/hpcc-css-icess.2015.164.

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Okoye, Kingsley, Abdel Rahman H. Tawil, Usman Naeem, Rabih Bashroush, and Elyes Lamine. "A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning." In 2014 IEEE International Conference on High Performance Computing and Communications (HPCC), 2014 IEEE 6th International Symposium on Cyberspace Safety and Security (CSS) and 2014 IEEE 11th International Conference on Embedded Software and Systems (ICESS). IEEE, 2014. http://dx.doi.org/10.1109/hpcc.2014.143.

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Kingsley, Okoye, Abdel-Rahman H. Tawil, Usman Naeem, Syed Islam, and Elyes Lamine. "Using semantic-based approach to manage perspectives of process mining: Application on improving learning process domain data." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841016.

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Li, Xiaojian, and Shihong Chen. "Research on Personalized User Model Based on Semantic Mining from Educational Resources Searching Process." In 2009 International Joint Conference on Artificial Intelligence (JCAI). IEEE, 2009. http://dx.doi.org/10.1109/jcai.2009.67.

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Wang, Xiaoyang, Jimin Xiao, Bingfeng Zhang, and Limin Yu. "CARD: Semi-supervised Semantic Segmentation via Class-agnostic Relation based Denoising." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/202.

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Recent semi-supervised semantic segmentation methods focus on mining extra supervision from unlabeled data by generating pseudo labels. However, noisy labels are inevitable in this process which prevent effective self-supervision. This paper proposes that noisy labels can be corrected based on semantic connections among features. Since a segmentation classifier produces both high and low-quality predictions, we can trace back to feature encoder to investigate how a feature in a noisy group is related to those in the confident groups. Discarding the weak predictions from the classifier, rectifi

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