Christian Mancas*
Mathematics and Computer Science Department, Ovidius University, Constanta,
Romania
*Corresponding Author: Christian Mancas, Mathematics and Computer Science Department, Ovidius University, Constanta, Romania.
Received: March 20, 2020; Published: March 31, 2020
MatBase is a prototype intelligent data and knowledge base management system based on the Relational, Entity-Relationship, and (Elementary) Mathematical Data Models. The latter distinguishes itself especially by its rich panoply of constraint types: 61, partitioned into three categories (set, containing 16 types, mapping, containing 44 types, and object) and eight subcategories (general set, dyadic relation, general mapping, autofunction, general function product, homogeneous binary function product, function diagram, and object). They provide database and software application designers with the tools necessary for capturing and enforcing all business rules from any sub-universe of discourse, thus guaranteeing database instances plausibility, a sine qua non condition of data quality. This mathematical data model also includes Datalog, thus making MatBase also a deductive, so a knowledge base system. Currently, there are two MatBase versions (one developed in MS Access and the other in MS.NET, using C# and SQL Server), used both by two software developing companies and during labs of our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the Department of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucharest Polytechnic University. This paper presents MatBase’s metadata catalog and its management.
Keywords: Metadata and Data Quality; Semantic Approaches; Metadata Management; Metadata for Business Process Modeling; Data Structures and Algorithms for Data Management; DBMS Engine Architectures; (Elementary) Mathematical Data Model; Mat- Base
Citation: Christian Mancas. “MatBase Metadata Catalog Management”. Acta Scientific Computer Sciences 2.4 (2020): 25-29.
Copyright: © 2020 Christian Mancas. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.