A set of relational views that expose the standard form metadata stored in analytic workspaces, where it can be accessed by SQL. Applications that use OracleBI Beans query the Active Catalog.
See also database standard form.
Describes a measure or fact that can be summarized through addition, such as a
SUM function. An additive measure is the most common type. Examples include sales, cost, and profit.
Contrast with nonadditive.
The process of consolidating data values into a single value. For example, sales data could be collected on a daily basis and then be aggregated to the week level, the week data could be aggregated to the month level, and so on. The data can then be referred to as aggregate data.
The term aggregation is often used interchangeably with summarization, and aggregate data is used interchangeably with summary data. However, there are a wide range of aggregation methods available in addition to
The process of distributing aggregate data down a hierarchy to the detail level, sometimes using an existing set of data as the basis for the allocation. Allocation is often used in forecasting and budgeting systems. An example of a financial allocation is the automated distribution of a bonus pool, based on the current salaries and performance ratings of the employees.
A container for storing related dimensional objects, such as dimensions and cubes. An analytic workspace is stored in a relational table.
See also cube.
A dimension member at a higher level of aggregation than a particular member. For example, in a Time dimension, the year 2007 is the ancestor of the day 06-July-07. The member immediately above is the parent. In a dimension hierarchy, the data value of the ancestor is the aggregated value of the data values of its descendants.
A database object related to an OLAP cube dimension. An attribute stores descriptive characteristics for all dimension members, or members of a particular hierarchy, or only members at a particular level of a hierarchy.
When the values of an attribute are unique, they provide supplementary information that can be used for display (such as a descriptive name) or in analysis (such as the number of days in a time period). When the values of an attribute apply to a group of dimension members, they enable users to select data based on like characteristics. For example, in a database representing footwear, you might use a color attribute to select all boots, sneakers, and slippers of the same color.
See detail data.
A stored expression that executes in response to a query. For example, a calculated measure might generate the difference in costs from the prior period by using the
LAG_VARIANCE function on the
COSTS measure. Another calculated measure might calculate profits by subtracting the
COSTS measure from the
SALES measure. The expression resolves only the values requested by the query.
A single data value of an expression. In a dimensioned expression, a cell is identified by one value from each of the dimensions of the expression. For example, if you have a measure with the dimensions
CUSTOMER, then each combination of a month and a customer identifies a separate cell of that measure.
A dimension member that is part of a more aggregate member in a hierarchy. For example, in a Time dimension, the month Jan-06 might be the child of the quarter Q1-2006. A dimension member can be the child of a different parent in each hierarchy.
A compact format for storing sparse multidimensional data. Oracle OLAP provides two types of composites: a compressed composite for extremely sparse data, and a regular composite for moderately sparse data.
A cube with very sparse data that is stored in a compressed composite.
See also composite.
See compressed cube.
An organization of measures with identical dimensions and other shared characteristics. The edges of the cube contain the dimension members, and the body of the cube contains the data values. For example, sales data can be organized into a cube whose edges contain values from the Time, Product, and Customer dimensions and whose body contains Volume Sales and Dollar Sales data.
See calculated measure.
A dimension member whose data is calculated from the values of other members of the same dimension using the rules defined in a model.
A relational table, view, synonym, or other database object that provides detail data for cubes and cube dimensions.
A database designed for query and analysis rather than transaction processing. A data warehouse usually contains historical data that is derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables a business to consolidate data from several sources.
An analytic workspace that has been constructed with a specific set of objects, such as hierarchy dimensions, level dimensions, parent relations, and level relations. Each object must be defined with a set of properties that identify its role and its relationships with other objects in the analytic workspace. Standard form is required for an analytic workspace to be accessible to OLAP tools, however, it is not a prerequisite for multidimensional analysis.
Permit redundancy in a table. Contrast with normalize.
See calculated measure.
A dimension member at a lower level of aggregation than a particular member. For example, in a Time dimension, the day 06-July-07 is the descendant of year 2007. The member immediately below is the child. In a dimension hierarchy, the data values of the descendants roll up into the data values of the ancestors.
Data at the lowest level, which is acquired from another source.
Contrast with aggregation.
A structure that categorizes data. Among the most common dimensions for sales-oriented data are Time, Geography, and Product. Most dimensions have hierarchies and levels.
In a cube, a dimension is a list of values at all levels of aggregation. It is an index for identifying the values of a measure. For example, if Sales data has a separate sales figure for each month, then the data has a Time dimension that contains month values, which organize the data by month.
In a relational table, a dimension is a type of object that defines hierarchical (parent/child) relationships between pairs of column sets.
See also hierarchy.
See dimension member.
One element in the list that composes a cube dimension. For example, a Time dimension might have dimension members for days, months, quarters, and years.
A relational table that stores all or part of the values for a dimension in a star or snowflake schema. Dimension tables typically contain columns for the dimension keys, levels, and attributes.
See dimension member.
A relational view of data in an analytic workspace that contains the same types of data as a dimension table in a star schema, that is, columns for dimension members and attributes. A dimension view typically lists all dimension members in the key column, regardless of their level in the dimension hierarchy.
To navigate from one item to a set of related items. Drilling typically involves navigating up and down through the levels in a hierarchy.
Drilling down expands the view to include child values that are associated with parent values in the hierarchy.
Drilling up collapses the list of descendant values that are associated with a parent value in the hierarchy.
A specially formatted file for transferring data between analytic workspaces, or for storing versions of an analytic workspace (all of it or selected objects) outside the database.
A list of dimension members at all levels of a hierarchy, such that the aggregate members (totals and subtotals) are interspersed with the detail members. For example, a Time dimension might contain dimension members for days, months, quarters, and years.
A combination of one or more values (typically provided by a measure or a calculated measure), operators, and functions that evaluates to a value. An expression generally assumes the data type of its components.
The following are examples of expressions, where
SALES is a measure:
A table in a star schema that contains factual data. A fact table typically has two types of columns: those that contain facts and those that are foreign keys to dimension tables. The primary key of a fact table is usually a composite key that is made up of all of its foreign keys.
A fact table might contain either detail facts or aggregated facts. Fact tables that contain aggregated facts are typically called summary tables or materialized views. A fact table usually contains facts with the same level of aggregation.
See also materialized view.
A way to organize data at different levels of aggregation. Hierarchies are used to define data aggregation; for example, in a Time dimension, a hierarchy might be used to aggregate data from days to months to quarters to years. Hierarchies are also used to define a navigational drill path.
In a relational table, hierarchies can be defined as part of a dimension object.
A column or set of columns included in the definition of certain types of integrity constraints. Keys describe the relationships between the different tables and columns of a relational database.
See also dimension member.
See detail data.
A named position in a hierarchy. For example, a Time dimension might have a hierarchy that represents data at the month, quarter, and year levels. The levels might be named Month, Quarter, and Year. The names provide an easy way to reference a group of dimension members at the same distance from the base.
A hierarchy composed of levels. For example, Time is always level based with levels such as Month, Quarter, and Year. Most hierarchies are level based.
See also value-based hierarchy.
The definition of the relationship and data flow between source and target objects. For example, the metadata for a cube includes the mappings between each measure and the columns of a fact table or view.
A database object that provides access to aggregate data and can be recognized by the automatic refresh and the query rewrite subsystems.
Data that represents a business measure, such as sales or cost data. You can select, display, and analyze the data in a measure. The terms measure and fact are synonymous; measure is more commonly used in a multidimensional environment and fact is more commonly used in a relational environment.
Measures are dimensional objects that store data, such as Volume Sales and Dollar Sales. Measures belong to a cube.
A database object that organizes and label groups of measures. Users may have access to several schemas with measures named Sales or Costs, and measure folders provide a way to differentiate among them.
A relational view of data in analytic workspace that contains the same types of data as a fact table in a star schema. However, in addition to the base-level facts, a measure view also contains derived data, such as aggregates and inter-row calculations.
A set of inter-related equations specified using the members of a particular dimension. Line item dimensions often use models to calculate the values of dimension members.
A special data value that indicates that data is "not available" (
NA) or null. It is the value of any cell to which a specific data value has not been assigned or for which data cannot be calculated.
Describes a measure or fact that cannot be summarized through addition, such as Unit Price. Maximum is an example of a nonadditive aggregation method.
Contrast with additive.
In a relational database, the process of removing redundancy in data by separating the data into multiple tables. Contrast with denormalized.
In Oracle object technology, a form of user-defined data type that is an abstraction of a real-world entity. An object type is a schema object with the following components:
A name, which identifies the object type uniquely within a schema
Attributes, which model the structure and state of the real-world entity
Methods, which implement the behavior of the real-world entity, in either PL/SQL or Java
Online Analytical Processing. OLAP functionality is characterized by dynamic, dimensional analysis of historical data, which supports activities such as the following:
Calculating across dimensions and through hierarchies
Drilling up and down through hierarchies
Rotating to change the dimensional orientation
Contrast with OLTP.
Online Transaction Processing. OLTP systems are optimized for fast and reliable transaction handling. Compared to data analysis systems, most OLTP interactions involve a relatively small number of rows, but a larger group of tables.
Contrast with OLAP.
Calculated at run-time in response to a specific query. In a cube, calculated measures and custom members are typically calculated on the fly. Aggregate data can be precomputed, calculated on the fly, or a combination of the two methods.
Contrast with precompute.
A dimension member immediately above a particular member in a hierarchy. In a dimension hierarchy, the data value of the parent is the aggregated total of the data values of its children.
A one-to-many relationship between one parent and one or more children in a hierarchical dimension. For example, New York (at the state level) might be the parent of Albany, Buffalo, Poughkeepsie, and Rochester (at the city level).
Calculate and store as a data maintenance procedure. In a cube, aggregate data can be precomputed, calculated on the fly, or a combination of the two methods.
Contrast with on the fly.
A hierarchy that contains at least one member with a different base level, creating a "ragged" base level for the hierarchy. Organization dimensions are frequently ragged.
A hierarchy that contains at least one member whose parents are multiple levels above it, creating a hole in the hierarchy. For example, in a Geography dimension with levels for City, State, and Country, Washington D.C. is a city that does not have a State value; its parent is United States at the Country level.
A type of star schema in which the dimension tables are partly or fully normalized.
A result set in which all derived data has been calculated. Data fetched from an cube is always fully solved, because all of the data in the result set is calculated before it is returned to the SQL-based application. The result set from the cube is the same whether the data was precomputed or calculated on the fly.
See data source.
A concept that refers to multidimensional data in which a relatively high percentage of the combinations of dimension values do not contain actual data.
There are two types of sparsity:
Controlled sparsity occurs when a range of values of one or more dimensions has no data; for example, a new measure dimensioned by Month for which you do not have data for past months. The cells exist because you have past months in the Month dimension, but the cells are empty.
Random sparsity occurs when nulls are scattered throughout a measure, usually because some combinations of dimension members never have any data. For example, a district might only sell certain products and never have sales data for the other products.
Some dimensions may be sparse while others are dense. For example, every time period may have at least one data value across the other dimensions, making Time a dense dimension. However, some products may not be sold in some cities, and may not be available anywhere for some time periods; both Product and Geography may be sparse dimensions.
See also composite.
A join between a fact table and a number of dimension tables. Each dimension table is joined to the fact table using a primary key to foreign key join, but the dimension tables are not joined to each other.
A relational schema whose design represents a dimensional data model. The star schema consists of one or more fact tables and one or more dimension tables that are related through foreign keys.
See also snowflake schema.
The list of currently accessible values for a given dimension. The status of a dimension persists within a particular session, and does not change until it is changed deliberately. When an analytic workspace is first attached to a session, all members are in status.
See also dimension member.
A hierarchy defined only by the parent-child relationships among dimension members. The dimension members at a particular distance from the base level do not form a meaningful group for analysis, so the levels are not named. For example, an employee dimension might have a parent-child relation that identifies each employee's supervisor. However, levels that group together first-, second-, and third-level supervisors and so forth may not be meaningful for analysis.