An OLAP cube is a multi-dimensional array of data.[1] Online analytical processing (OLAP)[2] is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three.

A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario (actual and budget) are the data's dimensions.[3]

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Cube is a shorthand for multidimensional dataset, given that data can have an arbitrary number of dimensions. The term hypercube is sometimes used, especially for data with more than three dimensions. A cube is not a "cube" in the strict mathematical sense, as the sides are not all necessarily equal. But this term is used widely.

A Slice is a term for a subset of the data, generated by picking a value for one dimension and only showing the data for that value (for instance only the data at one point in time). Spreadsheets are only 2-dimensional, so by (continued) slicing or other techniques, it becomes possible to visualise multidimensional data in them.

OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

The elements of a dimension can be organized as a hierarchy,[4] a set of parent-child relationships, typically where a parent member summarizes its children. Parent elements can further be aggregated as the children of another parent.[5]

For example, May 2005's parent is Second Quarter 2005 which is in turn the child of Year 2005. Similarly cities are the children of regions; products roll into product groups and individual expense items into types of expenditure.

Conceiving data as a cube with hierarchical dimensions leads to conceptually straightforward operations to facilitate analysis. Aligning the data content with a familiar visualization enhances analyst learning and productivity.[5] The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up is sometimes called "slice and dice". Common operations include slice and dice, drill down, roll up, and pivot.

Slice is the act of picking a rectangular subset of a cube by choosing a single value for one of its dimensions, creating a new cube with one fewer dimension.[5] The picture shows a slicing operation: The sales figures of all sales regions and all product categories of the company in the year 2005 and 2006 are "sliced" out of the data cube.

Dice: The dice operation produces a subcube by allowing the analyst to pick specific values of multiple dimensions.[6] The picture shows a dicing operation: The new cube shows the sales figures of a limited number of product categories, the time and region dimensions cover the same range as before.

Drill Down/Up allows the user to navigate among levels of data ranging from the most summarized (up) to the most detailed (down).[5]The picture shows a drill-down operation: The analyst moves from the summary category "Outdoor protective equipment" to see the sales figures for the individual products.

Roll-up: A roll-up involves summarizing the data along a dimension. The summarization rule might be an aggregate function, such as computing totals along a hierarchy or applying a set of formulas such as "profit = sales - expenses".[5] General aggregation functions may be costly to compute when rolling up: if they cannot be determined from the cells of the cube, they must be computed from the base data, either computing them online (slow) or precomputing them for possible rollouts (large space). Aggregation functions that can be determined from the cells are known as decomposable aggregation functions, and allow efficient computation.[7] For example, it is easy to support COUNT, MAX, MIN, and SUM in OLAP, since these can be computed for each cell of the OLAP cube and then rolled up, since on overall sum (or count etc.) is the sum of sub-sums, but it is difficult to support MEDIAN, as that must be computed for every view separately: the median of a set is not the median of medians of subsets.

Pivot allows an analyst to rotate the cube in space to see its various faces. For example, cities could be arranged vertically and products horizontally while viewing data for a particular quarter. Pivoting could replace products with time periods to see data across time for a single product.[5][8]

Insofar as two-dimensional output devices cannot readily characterize three dimensions, it is more practical to project "slices" of the data cube (we say project in the classic vector analytic sense of dimensional reduction, not in the SQL sense, although the two are conceptually similar),

In Service Manager, data that is present in the data warehouse can be consolidated from various sources. It's presented through Service Manager by using predefined and customized Microsoft Online Analytical Processing (OLAP) data cubes. In short, advanced analytics in Service Manager consist of publishing, viewing, and manipulating cube data, usually in either Microsoft Excel or Microsoft SharePoint. Excel is primarily used by itself to view and manipulate data. SharePoint is used primarily as a means of publishing and sharing cube data.

Service Manager includes a System Center-wide data warehouse. Therefore, data from Operations Manager, Configuration Manager, and Service Manager can be consolidated into the data warehouse, where you can easily use multiple data views to get any information that you might want. This is also an interface where you can put data into the same data warehouse from your own custom sources, such as SAP applications or a third-party human resources application. This consolidation creates a common data model and enables enriched analyses to help you build a data warehouse across your Information Technology (IT) organization that can serve all your business intelligence and reporting needs.

When your data is in a common model, you can manipulate information and have common definitions and a common taxonomy for your whole enterprise. You can do this by deploying OLAP data cubes and accessing the information from the cubes, using standard tools such as Excel and SharePoint. This makes it possible for your users to employ skills that they already know. You control the definition of your business logic in a centralized manner. For example, you can define key performance indicators, such as the incident time-to-resolution thresholds, and which values for the thresholds are green, yellow, or red. You can control these choices in a centralized manner and empower your users to easily use the data, yet have the common definition appear in their Excel reports or their SharePoint dashboards.

An OLAP cube is a data structure that overcomes the limitations of relational databases by providing rapid analysis of data. Cubes can display and sum large amounts of data while also providing users with searchable access to any data points. This way, the data can be rolled up, sliced, and diced as needed to handle the widest variety of questions that are relevant to a user's area of interest.

Software vendors or information technology (IT) developers with a working knowledge of OLAP cubes can create management packs to define their own extensible and customizable OLAP cubes that are built on the data warehouse infrastructure. These cubes are stored in SQL Server Analysis Services (SSAS). Self-service business intelligence tools such as Excel and SQL Server Reporting Services (SSRS) can target these cubes in SSAS, and you can use them to analyze the data from multiple perspectives.

The databases that a business uses to store all its transactions and records are called online transaction processing (OLTP) databases. These databases usually have records that are entered one at a time and that contain a wealth of information that can be used by strategists to make informed decisions about their business. The databases that are used to store the data, however, weren't designed for analysis. Therefore, retrieving answers from these databases is costly in terms of time and effort. OLAP databases are specialized databases that are designed to help extract this business intelligence information from the data.

OLAP cubes can be considered as the final piece of the puzzle for a data warehousing solution. An OLAP cube, also known as multidimensional cube or hypercube, is a data structure in SQL Server Analysis Services (SSAS) that is built, using OLAP databases, to allow near-instantaneous analysis of data. The topology of this system is shown in the following illustration.

The useful feature of an OLAP cube is that the data in the cube can be contained in an aggregated form. To the user, the cube seems to have the answers in advance because assortments of values are already precomputed. Without having to query the source OLAP database, the cube can return answers for a wide range of questions almost instantaneously.

The main goal of Service Manager OLAP cubes is to give software vendors or information technology (IT) developers the ability to perform near-instantaneous analysis of data for both historical analysis and trending purposes. Service Manager does this by:

The following illustration shows an image from SQL Server Business Intelligence Development Studio (BIDS) that depicts the main parts that are required for online analytical processing (OLAP) cubes. These parts are the data source, data source view, cubes, and dimensions. The following sections describe the OLAP cube parts and the actions that users can take using them. 152ee80cbc

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