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Visual representation often may also be exported as a production-ready source code made in DB-compatible languages like SQL. The database schema is the structure of a database described in a formal language supported typically by a relational database management system (RDBMS).
This type of "translating" from one format to another is often called "metadata mapping" or "field mapping," and is related to "data mapping", and "semantic mapping". Crosswalks also have several technical capabilities. They help databases using different metadata schemes to share information. They help metadata harvesters create union catalogs.
Generates a template call rather than a table row; the template must then generate the table row in turn or the table header/footer need to be suppressed using skip_table parameter. Parameters passed to the template are the lower-case keys from the columns parameter, e.g. label or P18. Optional header_template
Snowflake schema used by example query. The example schema shown to the right is a snowflaked version of the star schema example provided in the star schema article. The following example query is the snowflake schema equivalent of the star schema example code which returns the total number of television units sold by brand and by country for 1997.
In Figure 12 an example of a concept with properties is visualized. The concept FEATURE has four properties, respectively: priority, type, risk and status. In Table 1 a list presented Each CONCEPT requires a proper definition which is preferably copied from a standard glossary. All CONCEPT names in the text are with capital characters.
A database catalog of a database instance consists of metadata in which definitions of database objects such as base tables, views (virtual tables), synonyms, value ranges, indexes, users, and user groups are stored. [1] [2] It is an architecture product that documents the database's content and data quality. [3]
Metadata conflicts e.g. same concepts are represented at schema level and instance level. Data conflicts e.g. missing attributes; Schema conflicts e.g. table versus table conflict which includes naming conflicts, data conflicts etc. In creating a federated schema, one has to resolve such heterogeneities before integrating the component DB schemas.
An example is an invoice, which in either multivalue or relational data could be seen as (A) Invoice Header Table - one entry per invoice, and (B) Invoice Detail Table - one entry per line item. In the multivalue model, we have the option of storing the data as on table, with an embedded table to represent the detail: (A) Invoice Table - one ...