Improving Document Evolvability based on Normalized Systems Theory
Authors
Year
2022
Published
Information Systems and Technologies. Springer, Cham, 2022. p. 131-140. ISSN 2367-3370. ISBN 978-3-031-04818-0.
Type
Proceedings paper
Departments
Annotation
During the last decade, there was a huge shift in the problem of evolvability. However, still, the problem remains fully unresolved. Our focus is to improve evolvability in the domain of data stewardship planning. The main problem causing the low evolvability is the use of traditional documents – data management plans. Our approach redesigns the workflow of creating the data management plan with a possibility to extend an application to similar well-structured documents in different domains. We base our approach on principles and recommendations from the Normalized Systems Theory and on the usage of ontologies. The result is a new workflow that should increase the evolvability compared to the current state.
Proposing Ontology-Driven Content Modularization in Documents Based on the Normalized Systems Theory
Authors
Year
2020
Published
Trends and Innovations in Information Systems and Technologies. Springer, Cham, 2020. p. 45-54. ISSN 2194-5357. ISBN 978-3-030-45687-0.
Type
Proceedings paper
Departments
Annotation
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
Authors
Year
2019
Published
Enterprise and Organizational Modeling and Simulation. Springer, Cham, 2019. p. 70-81. 1. ISSN 1865-1348. ISBN 978-3-030-35645-3.
Type
Proceedings paper
Departments
Annotation
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
Authors
Year
2019
Published
Codata Science Journal. 2019, 18(1), 1-8. ISSN 1683-1470.
Type
Article
Departments
Annotation
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.