Proposing Ontology-Driven Content Modularization in Documents Based on the Normalized Systems Theory
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A problem of evolvability is widely discussed in the current world, and still, it has not been fully addressed yet. Our approach tries to improve evolvability in a domain of documents. Our approach is based on principles and recommendations from the Normalized Systems Theory. We try to redefine the process of how the document is created and maintained by involving ontologies. We offer a solution which should increase evolvability for a sort of documents which is created by a template and which is often updated. We demonstrate our solution to an example of a Data Management Plan document.
Proposing an Architecture of an Intelligent Evolvable Document Generation System Based on the Normalized Systems Theory
Enterprise and Organizational Modeling and Simulation. Springer, Cham, 2019. p. 70-81. 1. vol. 366. ISSN 1865-1348. ISBN 978-3-030-35645-3.
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In the current world, low evolvability of documents is a big challenge which has not been fully addressed. This paper focuses on types of documents which have mostly predefined structure, and we use them over and over. Examples of these documents are contracts, applications, legal documents or manuals. The key problem here is that the documents are not modular and evolvable. The problem of modularity and evolvability is addressed by Normalized Systems Theory. This theory is formally proven, and it has great practical results from the first application in a software area. This paper designs a way how to apply principles and recommendations from Normalized Systems Theory in the area of non-evolvable documents.
“Data Stewardship Wizard”: A Tool Bringing Together Researchers, Data Stewards, and Data Experts around Data Management Planning
The Data Stewardship Wizard is a tool for data management planning that is focused on getting the most value out of data management planning for the project itself rather than on fulfilling obligations. It is based on FAIR Data Stewardship, in which each data-related decision in a project acts to optimize the Findability, Accessibility, Interoperability and/or Reusability of the data. The background to this philosophy is that the first reuser of the data is the researcher themselves. The tool encourages the consulting of expertise and experts, can help researchers avoid risks they did not know they would encounter by confronting them with practical experience from others, and can help them discover helpful technologies they did not know existed. In this paper, we discuss the context and motivation for the tool, we explain its architecture and we present key functions, such as the knowledge model evolvability and migrations, assembling data management plans, metrics and evaluation of data management plans.