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In computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function .
Materialized views were introduced by Oracle Database, while IBM Db2 provides so-called "materialized query tables" (MQTs) for the same purpose. Microsoft SQL Server introduced in its 2000 version indexed views which only store a separate index from the table, but not the entire data. PostgreSQL implemented materialized views in its 9.3 release.
Note (2): Materialized views are not supported in Informix; the term is used in IBM's documentation to refer to a temporary table created to run the view's query when it is too complex, but one cannot for example define the way it is refreshed or build an index on it. The term is defined in the Informix Performance Guide.
In a relational database, the schema defines the tables, fields, relationships, views, indexes, packages, procedures, functions, queues, triggers, types, sequences, materialized views, synonyms, database links, directories, XML schemas, and other elements. A database generally stores its schema in a data dictionary. Although a schema is defined ...
A database object is a structure for storing, managing and presenting application- or user-specific data in a database. Depending on the database management system (DBMS), many different types of database objects can exist. [1] [2] The following is a list of the most common types of database objects found in most relational databases (RDBMS):
For example, think of A as Authors, and B as Books. An Author can write several Books, and a Book can be written by several Authors. In a relational database management system, such relationships are usually implemented by means of an associative table (also known as join table, junction table or cross-reference table), say, AB with two one-to-many relationships A → AB and B → AB.
Staff often failed to document these assaults, and made a point of engaging in violent behavior away from the view of security cameras. The central takeaway: problems had been allowed to fester because of “the state’s failed system of oversight and accountability.”
Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).