The aim of this page is to provide a glossary of common OLAP and data warehousing terms and expressions. This list is for all terms beginning with M

Mapping

After creating logical objects, you can map them to data sources in Oracle Database. Afterward, you can load data into your analytic workspace using the Maintenance Wizard. The mapping window has a tabular view and a graphical view.

• Tabular view. Drag-and-drop the names of individual columns from the schema navigation tree to the rows for the logical objects.
• Graphical view. Drag-and-drop icons, which represent tables and views, from the schema navigation tree onto the mapping canvas. Then you draw lines from the columns to the logical objects.

If you want to see the values in a particular source table or view, right-click it in either the schema tree or the mapping canvas. Choose View Data from the menu to fetch up to 1000 rows. The images below shows both tabular view and graphical view of the mapping editor within AWM
AWM10g Mapping Editor - Tabular View

AWM10g Mapping Editor - Graphical View

Materialized View

"Materialized views are query results that have been stored in advance so long-running calculations are not necessary when you actually execute your SQL statements. From a physical design point of view, materialized views resemble tables or partitioned tables and behave like indexes in that they are used transparently and improve performance” Oracle Database Data Warehousing Guide 10g Release 2 (10.2) B14223-02

"Typically, data flows from one or more online transaction processing (OLTP) database into a data warehouse on a monthly, weekly, or daily basis. The data is normally processed in astaging filebefore being added to the data warehouse. Data warehouses commonly range in size from tens of gigabytes to a few terabytes. Usually, the vast majority of the data is stored in a few very large fact tables. One technique employed in data warehouses to improve performance is the creation of summaries. Summaries are special types of aggregate views that improve query execution times by precalculating expensive joins and aggregation operations prior to execution and storing the results in a table in the database. For example, you can create a summary table to contain the sums of sales by region and by product. The summaries or aggregates that are referred to in this book and in literature on data warehousing are created in Oracle Database using a schema object calledamaterialized view. Materialized views can perform a number of roles, such as improving query performance or providing replicated data.In the past, organizations using summaries spent a significant amount of time and effort creating summaries manually, identifying which summaries to create, indexing the summaries, updating them, and advising their users on which ones to use. The introduction of summary management eased the workload of the database administrator and meant the user no longer needed to be aware of the summaries that had been defined. The database administrator creates one or more materialized views, which are the equivalent of a summary. The end user queries the tables and views at the detail data level. The query rewrite mechanism in the Oracle server automatically rewrites the SQL query to use the summary tables. This mechanism reduces response time for returning results from the query. Materialized views within the data warehouse are transparent to the end user or to the database application. Although materialized views are usually accessed through the query rewrite mechanism, an end user or database application can construct queries that directly access the materialized views. However, serious consideration should be given to whether users should be allowed to do this because any change to the materialized views will affect the queries that reference them."Oracle Database Data Warehousing Guide 10g Release 2 (10.2) B14223-02

Measure

“Data, usually numeric and additive, that can be examined and analyzed. Examples include sales, cost, and profit. Fact and measure are synonymous; fact is more commonly used with relational environments, measure is more commonly used with multidimensional environments.” A derived fact is “A fact (or measure) that is generated from existing data using a mathematical operation or a data transformation. Examples include averages, totals, percentages, and differences.” Oracle Database Data Warehousing Guide 10g Release 2 (10.2) B14223-02
A fact is an observation. A number. A measure. The stock was “13.2202”

Measures are just like arrays and are automatically associated to the physical fact table column and related dimension tables. This transformation from fact table column to measure insulates the user from the complexity of the underlying schema and from the need to understand how the various parts of the schema are joined together.
Measures can share dimensions. So for example price and cost would probably share the same dimensions: Product, Channel and Time. However, a measure such as quantity sold might be dimensioned by Product, Geography, Channel and Time. Within an OLAP environment, it is extremely easy to create new measures such as sales revenues and sales costs by simply multiplying quantity * price and quantity * unit cost respectively. The resulting variables are in fact dimensional and share the same structure as quantity, so the new measure are dimensioned by Product, Geography, Channel and Time.

See also Calculated Measure

Measure Folder

Measure folders are used by OLAP tools, so that the measures can be located and identified quickly by users. They may have access to several analytic workspaces or relational schemas with measures named Sales or Costs, and they will have no means of differentiating them outside of a measure folder. Measure folders can be embedded within other folders to create a nested hierarchy.

Measure View

See cube view

Metadata

See also Standard Form.

Model