13 June 2009

🛢DBMS: Conceptual Data Model (Definitions)

"Represents the overall logical structure of a database, which is independent of any software or data storage structure. A Conceptual model often contains data objects not yet implemented in the physical databases. It gives a formal representation of the data needed to run an enterprise or a business activity." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"An organization of data that describes the relationships among the primitive data elements. For example, in the ER model, it is a diagram of the entities, their relationships, and their attributes." (Sam Lightstone et al, "Physical Database Design: The Database Professional’s Guide to Exploiting Indexes, Views, Storage, and More", 2007)

"An abstract model or representation of data for a particular domain, business enterprise, field of study, etc., independent of any specific software or information system. Usually expressed in terms of entities and relationships." (J P Getty Trust, "Introduction to Metadata" 2nd Ed., 2008)

"An organization of data that describes the relationships among the primitive data elements. For example, in the ER model, it is a diagram of the entities, their relationships, and their attributes." (Toby J Teorey, ", Database Modeling and Design" 4th Ed, 2010)

"In the ANSI four-schema architecture, this is a description of a portion of an enterprise in terms of the fundamental things of significant interest to it. They are fundamental in that most things seen by business owners are examples of these. The model is constructed in a rigorous manner, being fully normalized, eschewing many-to-many relationships and is expressed in terms of binary relationships only." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"A data model that is presented at a high level of abstraction, hiding the underlying details, and making it easier for people to comprehend. A conceptual model should reflect the phenomena in the users' world being modeled as directly as possible, as close to the way the users think. For example, many-to-many relationships are common in conceptual models." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A high-level data model identifying major entities and relationships, not fully attributed and therefore not necessarily normalized." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures" 2nd Ed., 2012)

"A high-level data modeling that specifies an abstract map of concepts and their relationships. SQL’s inherent hierarchical data modeling does this easily and naturally." (Michael M David & Lee Fesperman, "Advanced SQL Dynamic Data Modeling and Hierarchical Processing", 2013)

"A detailed model that captures the overall structure of organizational data that is independent of any database management system or other implementation considerations." (Jeffrey A. Hoffer et al, "Modern Systems Analysis and Design" 7th Ed., 2014)

"The output of the conceptual design process. The conceptual model provides a global view of an entire database. Describes the main data objects, avoiding details." (Carlos Coronel & Steven Morris, "Database Systems: Design, Implementation, & Management"  Ed. 11, 2014)

"A model of a database expressed at platform independent level; describes entities from a considered domain, properties of these entities, and the relationships between entities." (Iwona Dubielewicz et al, "Quality-Driven Database System Development within MDA Approach", 2015)

"The most abstract form of data model. It includes the important entities and the relationships among them and contains only major attributes." (Besma Khalfi et al, "Enhanced F-Perceptory Approach for Dealing with Geographic Data Imprecision from the Conceptual Modeling to the Fuzzy Geographical Database Building", 2017)

No comments:

Related Posts Plugin for WordPress, Blogger...

About Me

My photo
Koeln, NRW, Germany
IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.