Metamodel Specialisation based Tool Extension

This paper outlines our Deep Learning Lifecycle Data Management system. It consists of two major parts: the LDM Core Tool - a simple data logging tool; and an Extension Mechanism - this mechanism allows the user to extend the simple LDM Core Tool to match their specific requirements. Current extensi...

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Bibliographic Details
Published inBaltic Journal of Modern Computing Vol. 10; no. 1; pp. 17 - 35
Main Authors Barzdins, Paulis, Kalnins, Audris, Celms, Edgars, Barzdins, Janis, Sprogis, Arturs, Grasmanis, Mikus, Rikacovs, Sergejs, Barzdins, Guntis
Format Journal Article
LanguageEnglish
Published Riga University of Latvia 2022
Subjects
Data logging
Data management
Deep learning
Machine learning
Metamodels
Specialization
Work stations
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Summary:This paper outlines our Deep Learning Lifecycle Data Management system. It consists of two major parts: the LDM Core Tool - a simple data logging tool; and an Extension Mechanism - this mechanism allows the user to extend the simple LDM Core Tool to match their specific requirements. Current extensions support adding new visualisations for data stored on the server. Our approach allows the Core Tool to be a complete black box; we need only a metamodel denoting the logical structure of the stored data. By then specialising this metamodel we can define an Extension Metamodel which, when communicated to the tool through configuration, allows us to define and thus add the extensions.
ISSN:2255-8950
2255-8942
2255-8950
DOI:10.22364/bjmc.2022.10.1.02