One method of preparing a CSV file is to load the data into a spreadsheet. Note that you might need to use a specialist spreadsheet package that can cope with extremely large volume of data.
Example file from the Sample folder: Customer Response.csv
Create the spreadsheet.
WITNESS Miner needs to be alerted to the presence of categorical (text) fields. You can either do this when you create the spreadsheet (by prefixing each categorical field heading with an asterisk * symbol) or you can select each categorical field manually on the data source options dialog once you've loaded the .csv file.
In the example illustrated above, the categorical fields are Software, User Group and Response.
Save the spreadsheet in .csv format.
Open WITNESS Miner, create a data source node, then double-click on its icon in the workspace to open the data source options dialog.
In the data file field, enter the name and path of the .csv file (use the select button to display the file selector to find the file, if necessary).
If you have already flagged the categorical field headings with an asterisk symbol, select the use (*) flag in datafile option. If you haven't flagged the categorical fields, choose the select manually option, then click on the select button to display the select categorical fields dialog. Choose the categorical field headings from the list of available field headings.
Finally, choose whether to load all of the records available from the database, or to sample a specified number of records from the database as it is being loaded. This method is more efficient than loading the whole database (which might not be possible due to memory limitations) and then sampling the records.
The remaining fields on the data source options dialog do not apply to text files, so close the data source options dialog.
The text file is now linked to the data source. If you create a view node, link it to the data source and then run the view node, you can view the database. The database that results from the Customer Response.csv file looks like this in the data view:
