什么是OLAP

On-Line Analytical Processing (OLAP) is a category of software technology that enables analysts, managers and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.

OLAP functionality is characterized by dynamic multi-dimensional analysis of consolidated enterprise data supporting end user analytical and navigational activities including:

• calculations and modeling applied across dimensions, through hierarchies and/or across members
• trend analysis over sequential time periods
• slicing subsets for on-screen viewing
• drill-down to deeper levels of consolidation
• reach-through to underlying detail data
• rotation to new dimensional comparisons in the viewing area

OLAP is implemented in a multi-user client/server mode and offers consistently rapid response to queries, regardless of database size and complexity. OLAP helps the user synthesize enterprise information through comparative, personalized viewing, as well as through analysis of historical and projected data in various "what-if" data model scenarios. This is achieved through use of an OLAP Server.

OLAP SERVER

An OLAP server is a high-capacity, multi-user data manipulation engine specifically designed to support and operate on multi-dimensional data structures. A multi- dimensional structure is arranged so that every data item is located and accessed based on the intersection of the dimension members which define that item. The design of the server and the structure of the data are optimized for rapid ad-hoc information retrieval in any orientation, as well as for fast, flexible calculation and transformation of raw data based on formulaic relationships. The OLAP Server may either physically stage the processed multi-dimensional information to deliver consistent and rapid response times to end users, or it may populate its data structures in real-time from relational or other databases, or offer a choice of both. Given the current state of technology and the end user requirement for consistent and rapid response times, staging the multi-dimensional data in the OLAP Server is often the preferred method.

OLAP GLOSSARY

Defined terms:

• AGGREGATE
• ANALYSIS, MULTI-DIMENSIONAL
• ARRAY, MULTI-DIMENSIONAL
• CALCULATED MEMBER
• CELL
• CHILDREN
• COLUMN DIMENSION
• CONSOLIDATE
• CUBE
• DENSE
• DERIVED DATA
• DERIVED MEMBERS
• DETAIL MEMBER
• DIMENSION
• DRILL DOWN/UP
• FORMULA
• FORMULA, CROSS-DIMENSIONAL
• GENERATION, HIERARCHICAL
• HIERARCHICAL RELATIONSHIPS
• HORIZONTAL DIMENSION
• HYPERCUBE
• INPUT MEMBERS
• LEVEL, HIERARCHICAL
• MEMBER, DIMENSION
• MEMBER COMBINATION
• MISSING DATA, MISSING VALUE
• MULTI-DIMENSIONAL DATA STRUCTURE
• MULTI-DIMENSIONAL QUERY LANGUAGE
• NAVIGATION
• NESTING (OF MULTI-DIMENSIONAL COLUMNS AND ROWS)
• NON-MISSING DATA
• OLAP CLIENT
• PAGE DIMENSION
• PAGE DISPLAY
• PARENT
• PIVOT
• PRE-CALCULATED/PRE-CONSOLIDATED DATA
• REACH THROUGH
• ROLL-UP
• ROTATE
• ROW DIMENSION
• SCOPING
• SELECTION
• SLICE
• SLICE AND DICE
• SPARSE
• VERTICAL DIMENSION

Definitions:

See: Consolidate

The objective of multi-dimensional analysis is for end users to gain insight into the meaning contained in databases. The multi-dimensional approach to analysis aligns the data content with the analyst's mental model, hence reducing confusion and lowering the incidence of erroneous interpretations. It also eases navigating the database, screening for a particular subset of data, asking for the data in a particular orientation and defining analytical calculations. Furthermore, because the data is physically stored in a multi- dimensional structure, the speed of these operations is many times faster and more consistent than is possible in other database structures. This combination of simplicity and speed is one of the key benefits of multi-dimensional analysis.

A group of data cells arranged by the dimensions of the data. For example, a spreadsheet exemplifies a two-dimensional array with the data cells arranged in rows and columns, each being a dimension. A three-dimensional array can be visualized as a cube with each dimension forming a side of the cube, including any slice parallel with that side. Higher dimensional arrays have no physical metaphor, but they organize the data in the way users think of their enterprise. Typical enterprise dimensions are time, measures, products, geographical regions, sales channels, etc.
Synonyms: Multi-dimensional Structure, Cube, Hypercube

A calculated member is a member of a dimension whose value is determined from other members' values (e.g., by application of a mathematical or logical operation). Calculated members may be part of the OLAP server database or may have been specified by the user during an interactive session. A calculated member is any member that is not an input member.

A single datapoint that occurs at the intersection defined by selecting one member from each dimension in a multi-dimensional array. For example, if the dimensions are measures, time, product and geography, then the dimension members: Sales, January 1994, Candy Bars and United States specify a precise intersection along all dimensions that uniquely identifies a single data cell, which contains the value of candy bar sales in the United States for the month of January 1994.
See: Member Combination

Members of a dimension that are included in a calculation to produce a consolidated total for a parent member. Children may themselves be consolidated levels, which requires that they have children. A member may be a child for more than one parent, and a child's multiple parents may not necessarily be at the same hierarchical level, thereby allowing complex, multiple hierarchical aggregations within any dimension.

See: Page Display

Multi-dimensional databases generally have hierarchies or formula-based relationships of data within each dimension. Consolidation involves computing all of these data relationships for one or more dimensions, for example, adding up all Departments to get Total Division data. While such relationships are normally summations, any type of computational relationship or formula might be defined.
Synonyms: Roll-up, Aggregate
See: Formula, Hierarchical Relationships, Children, Parents

See: Array, Multi-dimensional

A multi-dimensional database is dense if a relatively high percentage of the possible combinations of its dimension members contain data values. This is the opposite of sparse.

Derived data is produced by applying calculations to input data at the time the request for that data is made, i.e., the data has not been pre-computed and stored on the database. The purpose of using derived data is to save storage space and calculation time, particularly for calculated data that may be infrequently called for or that is susceptible to a high degree of interactive personalization by the user. The tradeoff is slower retrievals.
See: Pre-calculated Data

Derived members are members whose associated data is derived data.

A detail member of a dimension is the lowest level number in its hierarchy.
See: Level

A dimension is a structural attribute of a cube that is a list of members, all of which are of a similar type in the user's perception of the data. For example, all months, quarters, years, etc., make up a time dimension; likewise all cities, regions, countries, etc., make up a geography dimension. A dimension acts as an index for identifying values within a multi-dimensional array. If one member of the dimension is selected, then the remaining dimensions in which a range of members (or all members) are selected defines a sub-cube. If all but two dimensions have a single member selected, the remaining two dimensions define a spreadsheet (or a "slice" or a "page"). If all dimensions have a single member selected, then a single cell is defined. Dimensions offer a very concise, intuitive way of organizing and selecting data for retrieval, exploration and analysis.

Drilling down or up is a specific analytical technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down). The drilling paths may be defined by the hierarchies within dimensions or other relationships that may be dynamic within or between dimensions. For example, when viewing sales data for North America, a drill-down operation in the Region dimension would then display Canada, the eastern United States and the Western United States. A further drill- down on Canada might display Toronto, Vancouver, Montreal, etc.

A formula is a database object, which is a calculation, rule or other expression for manipulating the data within a multi-dimensional database. Formulae define relationships among members. Formulae are used by OLAP database builders to provide great richness of content to the server database. Formulae are used by end users to model enterprise relationships and to personalize the data for greater visualization and insight.

Formulae with all operands within a dimension are common, even in non-OLAP systems: e.g., Profit = Sales -

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