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  2. Aggregate function - Wikipedia

    en.wikipedia.org/wiki/Aggregate_function

    In other cases the aggregate cannot be computed without analyzing the entire set at once, though in some cases approximations can be distributed; examples include DISTINCT COUNT (Count-distinct problem), MEDIAN, and MODE. Such functions are called decomposable aggregation functions [4] or decomposable aggregate functions.

  3. Data Analysis Expressions - Wikipedia

    en.wikipedia.org/wiki/Data_Analysis_eXpressions

    Data Analysis Expressions (DAX) is the native formula and query language for Microsoft PowerPivot, Power BI Desktop and SQL Server Analysis Services (SSAS) Tabular models. DAX includes some of the functions that are used in Excel formulas with additional functions that are designed to work with relational data and perform dynamic aggregation.

  4. Online analytical processing - Wikipedia

    en.wikipedia.org/wiki/Online_analytical_processing

    For example, the overall sum of a roll-up is just the sum of the sub-sums in each cell. Functions that can be decomposed in this way are called decomposable aggregation functions, and include COUNT, MAX, MIN, and SUM, which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions. [13]

  5. XLeratorDB - Wikipedia

    en.wikipedia.org/wiki/XLeratorDB

    XLeratorDB uses three kinds of functions to perform analytic operations: scalar, aggregate, and a hybrid form which WestClinTech calls Range Queries. Scalar functions take a single value, perform an operation and return a single value. [10] An example of this type of function is LOG, which returns the logarithm of a number to a specified base. [11]

  6. Pivot table - Wikipedia

    en.wikipedia.org/wiki/Pivot_table

    For example, in Microsoft Excel one must first select the entire data in the original table and then go to the Insert tab and select "Pivot Table" (or "Pivot Chart"). The user then has the option of either inserting the pivot table into an existing sheet or creating a new sheet to house the pivot table.

  7. MultiDimensional eXpressions - Wikipedia

    en.wikipedia.org/wiki/MultiDimensional_eXpressions

    The MultiDimensional eXpressions (MDX) language provides a specialized syntax for querying and manipulating the multidimensional data stored in OLAP cubes. [1] While it is possible to translate some of these into traditional SQL, it would frequently require the synthesis of clumsy SQL expressions even for very simple MDX expressions.

  8. OLAP cube - Wikipedia

    en.wikipedia.org/wiki/OLAP_cube

    An example of an OLAP cube. 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.

  9. Aggregate (data warehouse) - Wikipedia

    en.wikipedia.org/wiki/Aggregate_(data_warehouse)

    An aggregate is a type of summary used in dimensional models of data warehouses to shorten the time it takes to provide answers to typical queries on large sets of data. The reason why aggregates can make such a dramatic increase in the performance of a data warehouse is the reduction of the number of rows to be accessed when responding to a query.