Common warehouse metamodel: an introduction to the standard by John Poole, Dan Chang, Douglas Tolbert, David Mellor

By John Poole, Dan Chang, Douglas Tolbert, David Mellor

The authentic advisor to programming with the progressive data-sharing technologyThe universal Warehouse Metamodel (CWM) is the hot OMG common that makes the sharing of information seamless. The CWM regular improvement staff presents builders with a whole review of what CWM is and the way it really works. After acquainting readers with the CWM structure and the way every one CWM part matches into current database and knowledge warehouse architectures, the authors supply professional assistance on tips on how to plan for, enforce, and install CWM technologies.Companion site positive factors updates on CWM applied sciences, descriptions of instruments, and hyperlinks to seller websites.

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This requires that the metamodel be a reasonably complete representation of its domain and that the metamodel further provide some means of extension, whereby any semantic gaps in the metamodel can be compensated for in a standard, agreed-upon manner by participating products. The model-based approach to meta data integration eliminates or significantly reduces the cost and complexities associated with the traditional pointto-point meta data integration architectures based on meta data bridges.

At this point, data in its most elemental form has been transformed into something more refined (a database row or record) and something certainly more useful. 1 The information supply chain. Data Mining The Value Proposition for CWM actual sales transactions, and we readily can examine specific properties of each transaction (date and time of sale, dollar amount, tax applied, and so on). Data at this stage of refinement is more useful than it was in its initial form, which consisted of not much more than a sequence of electrical signals.

1 shows an outline of that table and some possible data values as rows within the table. 6 is a model of the Product table. 5 is a model of the model (that is, a metamodel) of the Product table. Also, note that the SQL DDL for building the Product table could easily be derived from the Product table model (for example, via a software process that reads the UML instance diagram and translates it into equivalent table creation statements in SQL DDL). When a relational database engine processes the SQL DDL statement, it generates internal meta data describing the Product table and stores this meta data in the relational database catalog, which is essentially a local repository of meta data describing all databases currently maintained by the relational database engine.

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