On enforcing dyadic-type homogeneous binary function product constraints in MatBase
Abstract
Homogeneous binary function products are often encountered in the sub-universes modeled by databases, from genealogical trees to sports, from education to healthcare, etc. Their properties must be discovered and enforced by the software applications managing such data to guarantee plausibility. The (Elementary) Mathematical Data Model provides 18 dyadic-type homogeneous binary function product constraint types. MatBase, an intelligent data and knowledge base management system prototype, allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them. This paper describes the algorithms that MatBase uses for enforcing all these 18 homogeneous binary function product constraint types, which may also be used by developers not having access to MatBase.
Cite
@article{arxiv.2312.06502,
title = {On enforcing dyadic-type homogeneous binary function product constraints in MatBase},
author = {Christian Mancas},
journal= {arXiv preprint arXiv:2312.06502},
year = {2024}
}
Comments
submitted on Dec. 7, 2023, to the Journal of Data Science and Intelligent Systems (JDSIS), on Dec. 20, 2023, to the Journal of Computational and Cognitive Engineering, both of Bon View Publishing, Singapore, on Dec. 30 to the Journal of Current Research and Studies, and on Jan. 25, 2024, to the Journal of Computer Science Research, Bilingual Publishing Group, Singapore