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Titlebook: Pro SQL Server 2008 Analysis Services; Philo Janus,Guy Fouché Book 2009 Philo Janus and Guy Fouche 2009 Microsoft SQL Server.SQL.SQL Serve

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樓主: Baleful
21#
發(fā)表于 2025-3-25 04:36:47 | 只看該作者
Introduction to OLAP, is a technique for aggregating data to enable business users to dig into transactional data to solve business problems. You may be familiar with pivot tables from Microsoft Excel or other reporting solutions—for example, taking a list of order details (.) and creating a table that shows the total for each product ordered by month (.).
22#
發(fā)表于 2025-3-25 07:59:06 | 只看該作者
Creating a Data Source View,Step 1: Get data. That’s what we have to do if we’re going to build a cube.
23#
發(fā)表于 2025-3-25 15:26:16 | 只看該作者
MDX,So, after eight Chapters of referring to Multidimensional Expressions (MDX), you finally get to find out what those expressions actually are. Just as SQL is a structured query language for returning tabular results from relational databases, MDX is a query language for returning multidimensional data sets from multidimensional data sources.
24#
發(fā)表于 2025-3-25 17:56:11 | 只看該作者
25#
發(fā)表于 2025-3-25 21:58:20 | 只看該作者
26#
發(fā)表于 2025-3-26 01:11:54 | 只看該作者
the data accessible to business users, instead of needing to.Every business has reams of business data locked away in databases, business systems, and spreadsheets. While you may be able to build some reports by pulling a few of these repositories together, actually performing any kind of analysis o
27#
發(fā)表于 2025-3-26 07:42:05 | 只看該作者
Book 2009y pulling a few of these repositories together, actually performing any kind of analysis on the data that runs your business can range from problematic to impossible. .Pro SQL Server 2008 Analysis Services. will show you how to pull that data together and present it for reporting and analysis in a w
28#
發(fā)表于 2025-3-26 08:40:53 | 只看該作者
29#
發(fā)表于 2025-3-26 15:20:41 | 只看該作者
30#
發(fā)表于 2025-3-26 20:01:21 | 只看該作者
Don Passey,Angela Siew-Hoong Lees are monitored to analyze the efficiency of the system. The results show that prediction model achieved with CGPANN is computationally cost effective and accurate (98.85%) that is unique as it is dependent on least amount of previous data for future data prediction.
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