data model


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data model

An unambiguous expression of items, relationship among items and data structure in a certain area of interest or context of use, which uses symbolic conventions to represent content so that its intended significance is not lost when communicated.
References in periodicals archive ?
A logical data model should be used as the blueprint for designing and creating physical databases.
OMG's key deliverables for 2011 include a glossary of P&C business terms and metatdata, a conceptual data model, and a logical data model.
Just like the system data model, there may be multiple technology models.
O-DMS is an object oriented data model, with a large number of classes with a more than adequate set of attributes.
The hierarchical data model organizes the record types in a tree structure, whereas the network data model organizes a database schema with the record types as the nodes of a graph.
Of course, a data model delivers value by enhancing communication and understanding, and it can be argued that these are quite valuable.
He is the author of "The Data Model Resource Book" series, which describe over 230 reusable, holistic data models and was rated #12 on the Computer Literacy Best Seller List.
The data model can be an effective tool for determining overlap and touchpoints.
The features of the TRIAD modeling suite, when coupled with the capabilities of Teradata's existing health-care Logical Data Model (LDM) and technology assets, will constitute the most comprehensive health-care industry LDM designed specifically for enterprise data warehouses (EDWs).
CA's AllFusion ERwin Data Modeler is a natural starting point for creating Web/SOA-based applications because the vocabulary of the business is actually contained in the data model," said Steve Lemmo, CTO & Founder of ObjectRiver.
Our solution provided semantic and data model integration across a complex IBM records and content management solution," Dirks commented.
1 release, ER/Studio builds on its enterprise model management and collaboration capabilities to help organizations take a global approach to two important aspects of data management: reducing risks associated with data security and enhancing data model quality.