A meta-analysis comparing relational and semantic models
Publication Type
Book Chapter
Publication Date
1-2013
Abstract
Data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance; however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.
Keywords
Data modeling, Meta-analysis, Relational data models, Relational modeling, Semantic data, Semantic data model, Semantic Data Models, Semantic Model, Statistical power, Systems development, User performance
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Innovations in Database Design, Web Applications, and Information Systems Management
Editor
SIAU, K.
First Page
394
Last Page
409
ISBN
10.4018/9781466620445
Identifier
10.4018/978-1-4666-2044-5.ch015
Publisher
IGI Global
City or Country
Hershey, PA
Citation
SIAU, Keng; NAH, Fiona Fui-hoon; and CAO, Qing.
A meta-analysis comparing relational and semantic models. (2013). Innovations in Database Design, Web Applications, and Information Systems Management. 394-409.
Available at: https://ink.library.smu.edu.sg/sis_research/10076
Additional URL
https://doi.org/10.4018/978-1-4666-2044-5.ch015
