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Juan Pablo Salazar-Fernandez, Jorge Munoz-Gama, Jorge Maldonado-Mahauad, Diego Bustamante and Marcos Sepúlveda
In this work, Process Mining techniques are used with a curricular analytics approach, to model the educational trajectories of engineering students during their first courses.
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Zeeshan Tariq, Darryl Charles, Sally McClean, Ian McChesney and Paul Taylor
A significant challenge for organisations is the timely identification of the abnormalities or deviations in their process executions. Abnormalities are generally due to missing vital aspects of a process or possession of unwanted behaviour in the proces...
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Zeeshan Tariq, Naveed Khan, Darryl Charles, Sally McClean, Ian McChesney and Paul Taylor
Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segme...
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Martin Wynn and Jose Irizar
This article examines how digital twins have been used in a multi-national corporation, what technologies have been used, what benefits have been delivered, and the significance of people- and process-related issues in achieving successful implementation...
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Jasmin Reiner, Kristin Protte and Jörg Hinrichs
Online detection of product defects using fast spectroscopic measurements is beneficial for producers in the dairy industry since it allows readjustment of product characteristics or redirection of product streams during production. Raman spectroscopy ha...
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Martin Sarnovsky, Peter Bednar and Miroslav Smatana
This paper describes the architecture of a cross-sectorial Big Data platform for the process industry domain. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of data from multiple i...
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Martin Sarnovsky, Peter Bednar and Miroslav Smatana
This paper describes the architecture of a cross-sectorial Big Data platform for the process industry domain. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of data from multiple i...
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Joao Mesquitela, Luis B. Elvas, Joao C Ferreira and Luis Nunes
Traffic accidents in urban areas lead to reduced quality of life and added pressure in the cities? infra-structures. In the context of smart city data is becoming available that allows a deeper analysis of the phenomenon. We propose a data fusion process...
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Grazia Dicuonzo,Graziana Galeone,Erika Zappimbulso,Vittorio Dell'Atti
Pág. 40 - 47
The need to query large volumes of heterogeneous data in differing formats from multiple sources, both internal and external and its centrality to the process of value creation is revolutionising traditional approaches to business models. Through the ado...
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Tulio Silveira-Santos, Anestis Papanikolaou, Thais Rangel and Jose Manuel Vassallo
App-based ride-hailing mobility services are becoming increasingly popular in cities worldwide. However, key drivers explaining the balance between supply and demand to set final prices remain to a considerable extent unknown. This research intends to un...
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Stela Stoykova and Nikola Shakev
The aim of this paper is to present a systematic literature review of the existing research, published between 2006 and 2023, in the field of artificial intelligence for management information systems. Of the 3946 studies that were considered by the auth...
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Murad Huseynli, Udo Bub and Michael Chima Ogbuachi
This paper outlines the path towards a method focusing on a process model for the integrated engineering of Digital Innovation (DI) and Design Science Research (DSR). The use of the DSR methodology allows for achieving both scientific rigor and practical...
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George Tsinarakis, Nikolaos Sarantinoudis and George Arampatzis
A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the ...
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Yaniv Mordecai, James P. Fairbanks and Edward F. Crawley
This paper introduces a holistic framework, underpinned by Category Theory, for the process of conceptual modeling of complex engineered systems, generically representing the models as graph data structures, rendering stakeholder-informing views like mat...
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Albert Weichselbraun, Philipp Kuntschik, Vincenzo Francolino, Mirco Saner, Urs Dahinden and Vinzenz Wyss
Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and human...
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Polina Buyvol, Irina Makarova, Aleksandr Voroshilov and Alla Krivonogova
The increasing complexity of vehicle design, the use of new engine types and fuels, and the increasing intelligence of automobiles are making it increasingly difficult to ensure trouble-free operation. Finding faulty parts quickly and accurately is becom...
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Andrés Redchuk and Federico Walas Mateo
This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of...
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Heba Ismail, Ashraf Khalil, Nada Hussein and Rawan Elabyad
This research proposes a well-being analytical framework using social media chatter data. The proposed framework infers analytics and provides insights into the public?s well-being relevant to education throughout and post the COVID-19 pandemic through a...
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Francisco José García-Peñalvo, Cristina Casado-Lumbreras, Ricardo Colomo-Palacios and Aman Yadav
Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutori...
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Yan Li, Manoj Thomas, Kweku-Muata Osei-Bryson and Jason Levy
With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environ...
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