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Next, i dragged the dsCampaign fields from the datasets pane and drop them on the table region to create the three columns. The report also demonstrates how expression-based conditional formatting can be used to alternate the background color of the table rows. The sales Forecast report uses a session data mining model to forecast future sales. Sales Forecasting Sales forecasting is a critical reporting requirement for most businesses. At the same time, sales forecasting has been traditionally difficult to implement and forecasts have been often wrong as a result of the incorrect output from the mathematical models used.
In this section, i would like to show you how ssas data mining can be used to create compelling and consistent sales forecast reports. From the users standpoint, the sales Forecast report (Figure 3) is simple. The end user would enter the year for the historical period and the number of the forecasted months. The report displays the sales figures for the selected year followed by the forecasted figures (shaded in gray). The data is broken down by the product model and territory on rows and time (in months) on columns. Choosing implementation approach The time series algorithm doesnt support passing an input dataset by using a prediction join statement. As a result, the only way to predict from an arbitrary dataset is to build a new Time series data model that is bound to that dataset. One way to do this is to create a session mining model which the server automatically disposes of at the end of the user session. From an ssrs standpoint, you have at least three options to author a report that uses a session mining model.
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Since the report needs the top ten customers who are most likely to buy a product from the selected category, i had to finalize the query in sql mode (right-click outside the two tables and choose. I needed to introduce an order clause in the query to sort by the predicted probability column and use. Select top 10 to cap the query. Constructing the report parameter As with the mdx command mode, the moment you introduce a parameter placeholder in the dmx summary query, the report Designer creates a report-level parameter. To make the parameter data-driven, i used a second dataset ( dsCategory ) that fetches the product categories from the AdventureWorks database. I used this dataset to derive the parameter available values. Once the report query and parameters have been taken care of, authoring the report layout is easy. I used a table region that is bound to the dsCampaign dataset.
Switch to the dmx command mode to create a dmx query. To produce the query needed for the targeted Campaign report, paper i used the design mode of the query builder. The vtargetMail view fulfils the role of the case table that contains the new customers. As with mdx, dmx queries can have parameters. The @ProductCategory parameter placeholder will be replaced during runtime with the value of the selected product category. If you want to test the parameter during design time, you can click on the query parameters toolbar button. In the query parameters dialog that follows, enter @ProductCategory as a parameter name and the category name as the parameter value.
lists the most likely buyers. Figure 1 shows the targeted Campaign report that meets this requirement. The end user can filter the report by product category. The report shows the top ten customers that may purchase a product from the selected category and the estimated probability. Constructing the dmx query, to implement a report that sources data from a mining model, you need to create a dmx (Data mining Extension) query to retrieve predicted data from a mining model. Constructing a dmx query statement is easy with the graphical query builder. Create a new dataset dsCampaign that uses sos olap as a data source. In the dataset dropdown (Data tab select the dsCampaign dataset to open it in the query builder. Select the pickaxe toolbar button to switch to the dmx command mode (Figure 2).
Such report could process historical patterns to present views of opportunities and trends. The two sample reports (Targeted Campaign and Sales Forecast) included in this article demonstrate how you can leverage data mining features of ssas to author smart reports. The first report presents a list of the homework top ten customers that are most likely to purchase a given product. The second report uses the past sales history to forecast future sales. The targeted Campaigning report uses a data mining model to display a list of potential buyers. These reports use the sos olap cube whose definition is included in the sos olap analysis Services project. The cube includes data mining models for targeted campaigning (Targeted m) and time forecasting (Sales m). Discussing the data mining models is beyond the scope of this article.
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Published: January 16, 2006, this article presents an excerpt from the book, applied Microsoft Analysis Services, by teo lachev. Learn how to author smart reports using Reporting Services (ssrs analysis Services (ssas and sql server clr stored procedures. Get the reports demonstrated in this article by downloading the sample code. On This Page, authoring Data mining. Reports, summary, related books: Authoring Data mining. Reports, business intelligence should homework help users analyze patterns from the past to plan for the future. Data mining presents ample opportunities to implement a new generation of reports with predictive analytics features.