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Extracting notional machines for databases

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Title: Extracting notional machines for databases
Authors: Miedema, Daphne, Fletcher, George, Aivaloglou, Fenia, Busuttil, Leonard, Farinetti, Laura, Goodfellow, Martin, Guerrini, Giovanna, Haldeman, Georgiana, Pan, Yuhan, Ramagoni, Sujeeth Goud, Satyavolu, Chandrika, Sooriamurthi, Raja, Tu, Xiaoying, Tudor, Liviana, ITiCSE 2025: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education
Publisher Information: Association for Computing Machinery
Publication Year: 2025
Collection: University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms: Education -- Databases, Computer science -- Data processing, Machine learning, Information visualization
Description: Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education. ; peer-reviewed
Document Type: conference object
Language: English
Relation: https://www.um.edu.mt/library/oar/handle/123456789/138988
DOI: 10.1145/3724389.3731277
Availability: https://www.um.edu.mt/library/oar/handle/123456789/138988
https://doi.org/10.1145/3724389.3731277
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number: edsbas.726421DA
Database: BASE
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  Data: Extracting notional machines for databases
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  Data: <searchLink fieldCode="AR" term="%22Miedema%2C+Daphne%22">Miedema, Daphne</searchLink><br /><searchLink fieldCode="AR" term="%22Fletcher%2C+George%22">Fletcher, George</searchLink><br /><searchLink fieldCode="AR" term="%22Aivaloglou%2C+Fenia%22">Aivaloglou, Fenia</searchLink><br /><searchLink fieldCode="AR" term="%22Busuttil%2C+Leonard%22">Busuttil, Leonard</searchLink><br /><searchLink fieldCode="AR" term="%22Farinetti%2C+Laura%22">Farinetti, Laura</searchLink><br /><searchLink fieldCode="AR" term="%22Goodfellow%2C+Martin%22">Goodfellow, Martin</searchLink><br /><searchLink fieldCode="AR" term="%22Guerrini%2C+Giovanna%22">Guerrini, Giovanna</searchLink><br /><searchLink fieldCode="AR" term="%22Haldeman%2C+Georgiana%22">Haldeman, Georgiana</searchLink><br /><searchLink fieldCode="AR" term="%22Pan%2C+Yuhan%22">Pan, Yuhan</searchLink><br /><searchLink fieldCode="AR" term="%22Ramagoni%2C+Sujeeth+Goud%22">Ramagoni, Sujeeth Goud</searchLink><br /><searchLink fieldCode="AR" term="%22Satyavolu%2C+Chandrika%22">Satyavolu, Chandrika</searchLink><br /><searchLink fieldCode="AR" term="%22Sooriamurthi%2C+Raja%22">Sooriamurthi, Raja</searchLink><br /><searchLink fieldCode="AR" term="%22Tu%2C+Xiaoying%22">Tu, Xiaoying</searchLink><br /><searchLink fieldCode="AR" term="%22Tudor%2C+Liviana%22">Tudor, Liviana</searchLink><br /><searchLink fieldCode="AR" term="%22ITiCSE+2025%3A+Proceedings+of+the+30th+ACM+Conference+on+Innovation+and+Technology+in+Computer+Science+Education%22">ITiCSE 2025: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education</searchLink>
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  Data: Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education. ; peer-reviewed
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