Designing and Building a Data Mining Project with Cube and OLAP

Transcription

1 Designing and Building a Data Mining Project with Cube and OLAP Project Description Buding a Business Analytic Data Mining Model using Microsoft BI Data Mining Tool and Data Warehouse and OLAP Cubes. Preparation of Project 1.Installing the SSDT BI for Visual Studio 2013 Tutorial: After successful installations, I start a project by following steps: 1) Open Visual Studio 2) File -> New Project 3) You should see following BI add-ons which lets you create Data mining projects. a. Analysis Service b. Integration Service c. Reporting Service 4) Create an Analysis Services project and name it, then click ok. 2. Installing Adventure Works 2014 database for SQL Server 2014

2 Design a Cube with Dimensions 1.Building a Data Source In The solution explorer, right click on Data Sources and click New Data Source and click New Data Source. This will bring up the Data Source Wizard, which will walk you through the creation process. The database I used is AdventureWorksDW2014.

3 Choose Inherit and then click next Enter data source name Lab3 and then click finish.

4 2.Building a Data Source View Right-click Data Source Views and choose New Data Source View. Make sure the Lab3 data source is selected and then click Next.

5 On Select Table and Views, I choose these dimensions and fact table : DimCustomer, DimGeography, DimProduct, DimDate, DimSalesTerritory and FactInternetSales Name the view : Internet Sales

6 The star schema of Internet Sales data. 3.Creating an Analysis Services Cube Right-click Cubes in the Solution Explorer and select New Cube to bring up the Cube Wizard and then click next. On the Select Creation Method screen, make sure Use Existing Tables is selected, and click Next.

7 Choose FactInternetSale as measure group and select measures that you want to include into cube. After that, based on available tables, select new dimensions to be created.

8 Enter name and finished.

9

10 4. Deploying the cube Right click project name and then click Deploy. I got the first Error due to the connection cannot be made

11 Because the start mode of SQL server Analysis Service is manual, the state of it is default stopped. So I open sql Server Configuration Manager to change the state of SQL server Analysis Service to Running. Repeat deploying process, I got another error: Contains an Impersonation Mode that is not supported for processing operations.

12 Fix: Double click on ds file (in this case which is Lab3.ds) to open Data source designer. In the impersonation tab, select windows user name and password and enter the windows login user id and password and now process the cube. Deploying project works now.

13 Right click Internet Sales.cube and hit process. After deployment completed successfully, click run. We can see cube deployed successfully.

14 5. Editting dimensions individually Right click dimensions name and click New Attribute from Column or drag the attribute from the pane of data source view to attributes pane. By using this way, I edit all dimensions individually.

15 Deploying the cube again

16 Writing MDX queries to retrieve data 1. Open SQL Server 2014 and select Analysis Services as server type. After connecting,we can find the cube Internet Sales has already in SQL server.

17 2. Right click project name and select New Query and then click MDX. We can find all the dimensions and their attributes in the panes.

18 3.Predict the color of each bike depending on the gender. Statement: select nonempty( [dim customer].[gender].children) on columns, ( nonempty([dim product].[model Name].children), nonempty([dim product].[color].children) )on rows from [Internet Sales] Screen shot details:

19 Conclusion: We can find out there is NO color-preference between male and female for each model. 4.Predict which cities we need more bikes supply to in next year. Decide top 5 cities that we need put more bike supply. The following statement show the bike sales in different cities each year. We can notice that there is no data in So I choose year 2013 as an example to show how to do prediction. select ([order date].[calendar year].members) on columns, order([dim geography].[city].members, [measures].[order quantity],desc)on rows from [Internet Sales] where ([measures].[order quantity]); Predict which cities we need more bikes supply to in Find out how many bikes have been sold in particular city in The following statement find top 20 cities which has best order quantity. select[measures].[order quantity] on 0, TopCount ({[dim geography].[city].members *[order date].[calendar year].[2013]},20,[measures].[order quantity] ) ON 1 from [Internet Sales]

20 Top 5 cities in order quantity: The same way to find out top five cities in SALES AMOUNT in 2013 select[measures].[sales amount] on 0, TopCount ({[dim geography].[city].members *[order date].[calendar year].[2013]},20,[measures].[sales amount] ) ON 1 from [Internet Sales]

21 Top 5 cities in Sales Amount: Conclusion: Top 5 cities in Sales Amount are London,Paris,Bellflower,Wollongong, Berlin Top 5 cities in Order Quantity are London, Paris, Cliffside, Berlin and Burien 5. Drill down on each quarter Using following statement to see the purchase of bikes in each quarter in year select ([order date].[calendar quarter].members)on columns, filter([dim geography].[city].members, [order date].[calendar year].[2013]) on rows from[internet sales] where([measures].[sales amount])

22 Save the table into excel and sorted in descending order of their total sales amount of year 2013 I select top 10 sale amounts cities and plot a quarter graph. We can see during quarter 4 th the sales of bikes usually get the peak value. We also can notice that some cities (e.g. Goulburn) have downward tendency.

23 quarter1 quarter2 quarter3 quarter4 London Paris Wollongong Warrnambool Bendigo Goulburn Bellflower Brisbane Townsville Geelong 6. Drill down on each month Using following statement to see the purchase of bikes in each quarter in year select ([order date].[month number of year].members)on columns, filter([dim geography].[city].members, [order date].[calendar year].[2013]) on rows from[internet sales] where([measures].[sales amount])

24 Save the table into excel and sorted in descending order of their total sales amount of year I select top 10 sale amounts cities and plot a month graph. We can see in Dec the sales of bikes usually get the peak value. January and December usually have the best sales which maybe because of holiday season sales. Another good sale month is June which maybe due to nice weather for outdoor London Paris Wollongong Warrnambool Bendigo Goulburn Bellflower Brisbane Townsville Geelong