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Oracle® Business Intelligence Concepts Guide
10g Release 2 (10.1.2.0.0) Part No. B13970-01 |
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Oracle® Business Intelligence Concepts Guide
10g Release 2 (10.1.2.0.0) Part No. B13970-01 |
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Active Catalog
A set of relational views that expose the standard form metadata stored in analytic workspaces, so that it can be accessed by SQL.
Discoverer, Spreadsheet Add-In, and custom applications that use BI Beans query the Active Catalog.
See also database standard form.
aggregation
The process of consolidating multiple data values into a single value. For example, sales data can be collected on a daily basis and aggregated to the week level, the weekly data could be aggregated to the month level, and so on. The data can then be referred to as aggregate data. Aggregation is synonymous with summarization, and aggregate data is synonymous with summary data. However, aggregation methods are not restricted to summarization. Other methods include average, first, last, minimum, and maximum.
analytic workspace
A multidimensional schema that is stored in a relational table. An analytic workspace can contain a variety of objects. Some of these objects may be integrally connected to other objects, while others are totally independent. Some objects store data that is useful to applications, and other objects may only exist for the purposes of the DBA or developer. There are several basic types of objects which play a variety of roles in the multidimensional model. In these respects, an analytic workspace is very similar to a relational schema.
The OLAP DML is the basic, low-level language for working in analytic workspaces. Tools are available in PL/SQL and Java that provide an interface to the OLAP DML for users already familiar with those languages.
See also OLAP DML.
attribute
A descriptive characteristic of either a single dimension member or a group of dimension members. When applied to a single member, attributes 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 applied to a group, attributes represent logical groupings that enable users to select data based on like characteristics. For example, in a database for footwear, you can use a shoe color attribute to select all boots, sneakers, and slippers that share the same color.
business intelligence
The information derived from monitoring key performance indicators, used to detect trends, identify warning signals, and provide a factual basis for critical business decisions.
cell
A single data value of a measure. In a multidimensional measure, a cell is identified by one member from each of the dimensions of the measure. For example, if you have a measure with the dimensions Month and District, then each combination of a month and a district identifies a separate cell of that measure.
crosstab
A layout of data in rows, columns, and pages. Dimension members are listed across the first row and down the first column; the data for measures appears in the cells that form the body of the crosstab. A crosstab can be used to display summary information and show how data varies across dimensions, such as sales by region by month. A crosstab is sometimes called a matrix.
Contrast with table. See also cell, dimension member, measure.
cube
A logical organization of measures with identical dimensions. The edges of a cube contain dimension members and the body of a cube contains data values. For example, a Sales cube might have edges containing members from the time, product, and customer dimensions. Volume Sales and Unit Sales might be two measures in the Sales cube.
See also measure.
custom measure
A derived measure that is calculated at run time and presented as one or more additional columns of data added to a result set. The result set includes a value for each dimension member currently selected. A custom measure typically employs a single-row function to perform a calculation on one or more stored measures. For example, an analyst might create a custom measure for the difference in costs from the prior period. Another analyst might create a custom measure that calculates profits by subtracting the Costs measure from the Sales measure.
See also dimension member, OLAP DML, measure.
custom member
A dimension member that is created at run time and defined as the parent of one or more existing dimension members. The values of a measure for a custom member are calculated using the aggregation rules for that dimension.
See also aggregation, dimension member, measure.
dashboard
A dynamic, graphical Web page containing all of a company's critical business information so that decision makers can view the status of all the key indicators in a single location.
data warehouse
A relational database that is 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.
database standard form
An analytic workspace that has been constructed with a specific set of objects. Each object must be defined with a set of properties that identify its role and its relationships with other objects in the analytic workspace. A set of catalogs within the analytic workspace organizes these properties, roles, and relationships as metadata and exposes it to SQL through the Active Catalog.
See also Active Catalog.
DBA
Database administrator. The person responsible for creating, installing, configuring, and maintaining Oracle Databases.
denormalize
Permit redundancy in a schema, typically to improve query performance. A star schema is almost always a denormalized relationship.
See also star schema. Contrast with normalize.
derived fact (or measure)
A fact (or measure) that is generated from existing data through a mathematical operation or a data transformation. Examples include averages, totals, percentages, and differences.
dimension
A structure that categorizes data. Among the most common dimensions for sales-oriented data are time, geography, and product. Most dimensions have hierarchies.
In an analytic workspace, a dimension is a container for a list of values. A dimension acts as 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 month dimension; that is, the data is organized by month.
In SQL, a dimension is a type of object that defines hierarchical (parent/child) relationships between pairs of column sets.
dimension member
One element in the list that makes up an analytic workspace dimension. Also called a dimension value. A computer company might have dimension members in the product dimension called LAPPC and DESKPC. Members in the geography dimension might include Boston and Paris. Members in the time dimension might include NOV02, DEC02, JAN03, FEB03, MAR03, and so forth.
dimension table
A relational table that stores all or part of the values for a logical dimension in a star or snowflake schema. Dimension tables describe the business entities of an enterprise, represented as hierarchical, categorical information such as time, departments, locations, and products. They are sometimes called lookup or reference tables.
dimension value
See dimension member.
drill
To navigate from one item to a set of related items. Drilling typically involves navigating up and down through the levels in a hierarchy. When selecting data, you can expand or collapse a hierarchy by drilling down or up in it, respectively.
In relational data stores, drilling may navigate to related dimensions.
drill down
To expand the view to include child values that are associated with parent values in the hierarchy.
drill up
To collapse the list of descendant values that are associated with a parent value in the hierarchy.
ETL
Extract, Transform, and Load. An ETL tool such as Warehouse Builder can extract data from its source, transform it using a variety of techniques (such as joins, aggregations, and data conversion functions), and load it into new data containers such as relational tables.
fact
See measure. See also derived fact (or measure).
fact table
A table in a star schema that contains facts. 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-level facts or facts that have been aggregated. 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.
hierarchy
A logical structure that uses ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation; for example, in a time dimension, a hierarchy might be used to aggregate data from the month level to the quarter level to the year level. A hierarchy can define a navigational drill path, regardless of whether the levels in the hierarchy represent aggregated totals.
In PL/SQL, hierarchies can be defined as part of a dimension object.
level
A position in a hierarchy. For example, a time dimension might have a hierarchy that represents data at the month, quarter, and year levels.
mapping
The definition of the relationship and data flow between source and target objects.
materialized view
A precomputed relational table comprising aggregated or joined data from fact and possibly dimension tables. Also known as a summary or aggregate table.
measure
Data that can be examined and analyzed, such as sales or cost data. You can select and display the data in a measure. Measures can be stored, or they can be calculated by means of formulas. 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.
There are both base measures and custom measures. Base measures, such as Volume Sales and Dollar Sales, are stored. Custom measures, such as Volume Share Year Ago, are calculated from base measures.
member
See dimension member.
metadata
Data that describes data and other structures, such as objects, business rules, and processes.
See also database standard form.
NA value
A special data value that indicates that data is "not available" (NA). It is the value of any cell to which a specific data value has not been assigned or for which data cannot be calculated.
normalize
In a relational database, the process of removing redundancy in data by separating the data into multiple tables. Normalization typically is used to reduce data storage requirements.
object
In an analytic workspace, a distinct item in the workspace dictionary. Analytic workspaces consist of one or more objects, such as variables, formulas, dimensions, relations, and programs, which are used to organize, store, and retrieve data. Each object is created with a particular object type and stores a particular type of information. Objects that are the same type (for example, three variables) can have different roles within the analytic workspace.
OLAP DML
The low-level data definition and manipulation language for analytic workspaces.
on the fly
Calculated at run time in response to a specific query. In an analytic workspace, custom measures and custom members are typically calculated on the fly. Aggregate data can be precalculated, calculated on the fly, or a combination of the two methods.
Contrast with precalculate.
online analytical processing (OLAP)
Functionality characterized by dynamic, multidimensional analysis of historical data, which supports activities such as the following:
Calculating across dimensions and through hierarchies
Analyzing trends
Drilling up and down through hierarchies
Rotating to change the dimensional orientation
online transaction processing (OLTP)
Systems 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.
portal
Groups of Web pages that host many different types of content that come from many different sources.
portlet
Reusable information components that summarize or provide access to different types of information sources. Examples of portlets include a dynamically updated report of quarterly earnings, a search field and button, and a simple user poll.
precalculate
Calculated and stored as a data maintenance procedure. In an analytic workspace, aggregate data can be precalculated, calculated on the fly, or a combination of the two methods.
Contrast with on the fly.
property
A characteristic of an object or component. Properties provide identifiers and descriptions, define object features (such as the number of decimal places or the color), or define object behaviors (such as when and how summary data is calculated). Properties are used extensively in standard form analytic workspaces.
See also object.
schema
A collection of related database objects. Relational schemas are grouped by database user ID and include tables, views, and other objects. Multidimensional schemas are called analytic workspaces and include dimensions, relations, variables, and other objects.
See also analytic workspace, snowflake schema, star schema.
snowflake schema
A type of star schema in which the dimension tables are partly or fully normalized.
See also normalize, schema, star schema.
solved data
A result set in which all derived data has been calculated. Data fetched from an analytic workspace 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 analytic workspace is the same whether the data was precalculated or calculated on the fly.
See also on the fly, precalculate.
source
See data source.
sparsity
A concept that refers to multidimensional data in which a relatively high percentage of the combinations of dimension values do not contain actual data. Such "empty," or NA, values consume storage space in an analytic workspace. To handle sparse data efficiently, you can create a composite.
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 variable 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 contain NA values.
Random sparsity occurs when NA values are scattered throughout the variable, usually because some combinations of dimension values never have any data. For example, a district might only sell certain products and never have data for other products. Other districts might sell some of those products and other ones, too.
See also NA value.
standard form
star schema
A relational schema whose design represents a multidimensional 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 schema, snowflake schema
stoplight formatting
The use of background colors in a crosstab to distinguish desirable, acceptable, and unacceptable values:
Red cells indicate unacceptable values, such as low Sales or high Costs.
Green cells indicate desirable values, such as high Sales or low Costs.
Yellow cells indicate acceptable values that do not need attention.
See also crosstab.
summary
See aggregation, materialized view.
