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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 .
TM1 Web/TM1 Contributor, IBM Cognos Insight, IBM Performance Modeler, IBM Cognos Cafe for Excel, Cognos BI, TM1 Perspectives for Excel Yes Yes icCube Yes Yes Yes Java, [23] R [24] Yes In the reporting Yes icCube reporting and all XMLA compliant visualization tools like Excel, etc Yes Yes Jedox OLAP Server: Yes Yes Yes Cube Rules, SVS Triggers ...
In a data warehouse, a measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made. A measure can either be categorical, algebraic or holistic. A measure can either be categorical, algebraic or holistic.
Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data sets. Array models provide natural indexing.
This was formerly known as Power BI for Office 365, now referred to as PowerBI.com or simply Power BI. Power BI Mobile Apps Power BI Mobile apps are for Android and iOS devices, as well as for Windows phones and tablets. Power BI Gateway Gateways are used to sync external data in and out of Power BI and are required for automated refreshes.
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 ...
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The nDCG values for all queries can be averaged to obtain a measure of the average performance of a ranking algorithm. Note that in a perfect ranking algorithm, the D C G p {\\displaystyle DCG_{p}} will be the same as the I D C G p {\\displaystyle IDCG_{p}} producing an nDCG of 1.0.
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