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1. In the Manage tab, click the Datapoints panel button.
2. Expand the Loaded Datapoints folder and select the datapoint you want to view.

   The Edit Datapoint panel opens, as shown in the following image.

   

3. Edit the Name or Description of the loadable datapoint.

   Note: Most of the information for a loadable datapoint is read-only.

4. Click the Dimensions tab to view the linked dimensions for the datapoint.
5. Click Save.

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To create a derived datapoint:

1. In the Manage tab, click the Datapoints panel button.
2. Click New.

   The New Datapoint panel opens.

3. Name the new derived datapoint.
4. Drag the datapoints you need for your calculation into the canvas. Each datapoint must be separated by its operation, as shown in the following image.

   

   Calculations can also include constants. To add a constant, drag the Constant object into position on the canvas, and type in the constant value inside the Constant object.

   Separate datapoints for WebFOCUS functions are typically created during the source load, since capturing these calculations is done best in the first-generation in the lineage, during harvesting.

   For example, if you want to capture counts of a particular condition, rather than trying to save all those attributes somewhere so you can perform the filtering later, you can determine When, that is what filters should be true, for the count. You can then pull that count into a loadable datapoint. Approaching data this way allows you to make calculations in the lineage after this harvesting phase simpler for you to manage.

5. Click Save. If the calculation is not complete, PMF recognizes this, and marks the derived datapoint as Incomplete. Incomplete derived datapoints do not participate in recalculation.

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PMF allows you to create an unlimited number of calculations for your measures using special datapoints that store and process calculations, known as derived datapoints. These calculations can be based on one or more existing datapoints, of any kind, including loadable, user-entered, generated, and other derived datapoints. Note the following:

• If you create a derived datapoint that uses data from loadable, user-entered, or generated datapoints, PMF will recalculate the results every time the data for these are changed. The data goes through the lineage, through all your steps of calculation, until it is copied to any measures linked to your datapoints.
• If you create a derived datapoint that uses data from another derived datapoint, PMF knows that the “parent” derived datapoint must be calculated before calculating your new derived datapoint. Logic built into PMF understands that calculations must use generations in the datapoint lineage.

  Recalculating a complex lineage chain through possibly hundreds of thousands or millions of row values can be an expensive operation, so you have full control over how much of this calculation is performed during normal processing hours.

  Note: During scheduled load cycles, since PMF is less used during scheduled load times (usually overnight), recalculation can always go through the entire lineage.

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You can preview the data that PMF will generate by clicking the Preview tab, as shown in the following image.

The Preview tab generally shows rows that are new, or will be updated or deleted.

Tips:

• The Preview tab shows data to be handled before any operations occur. It divides this data up into the following sections, based on what will happen to the rows that are displayed:
  • New rows to be created by generation.
  • Rows to be updated by generation.
  • Rows to be deleted by generation (depending on the Wipe Data setting on Advanced tab).
  • Rows that will be kept but whose values do not match the new data to be generated (depending on the Wipe Data setting on the Advanced tab).
• Opening the Preview tab will force the navigation bar to close, in order to use as much screen width as possible. You can reopen it by clicking the expand button at the top of the navigation bar.
• You can resort the preview contents in any order by clicking the column headings. Note that the Preview will show data based on the display limit set in the Load settings. To change this value, see Load Settings.

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Derived datapoints can have a complex, multi-part lineage, depending on their relationship to other derived datapoints.

• In the lineage directionality of derived datapoints, the data in derived datapoints always progresses to the left, from first-generation datapoints (loadable, user-entered, and generated) toward measures.
• PMF automatically handles figuring out the generation of each derived datapoint in the lineage, by analyzing the first point in the lineage where a derived datapoint sends its data onward.

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You can view lineage for all datapoints for any derived datapoint. Lineage shows the progress of data through PMF, from the external data harvested into datapoints, through any derived datapoints, and finally all terminal points in Measures. The Lineage tab displays the components in the generated source by default, as shown in the following image.

You can click on any node to expand it and display the contribution of that node to the lineage. You can also click Expand All or Collapse All to display or hide the entire lineage, as shown in the following image.

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To create a generated datapoint:

1. In the Manage tab, click the Datapoints panel button.
2. Click New.

   The New Datapoint panel opens.

3. Select Generated from the first drop-down menu.
4. Name the new datapoint.
5. Click the Dimensions tab and specify the dimensions and levels for which PMF will generate data, as shown in the following image.

   

   Setting dimensions affects some options on the Rules tab, so if you know the dimensions you want to use for generating, set them first.

6. Click the Rules tab and specify the rules PMF should use to generate data. The following options are available:

   Decimal Format

   Specifies the decimal format of the data generated:

   • The first character can be D (Decimal) or I (Integer).
   • The next characters are numbers to specify the total length of the field.
   • You can indicate a period and number of digits of decimal precision.

   Examples of typical decimal formats are: D12.2, I8, D20.6 and, I32.

   Method

   Controls how PMF will calculate the sample values:

   • Normal (Bell Curve) Distribution. PMF generates a range of values that favors the center of the numeric range you type in under Lower/Upper Bounds.
   • Uniform Random Distribution. PMF generates an even distribution of values that favors no point in the numeric range.

   Lower/Upper Bounds

   The lowest and highest number for the range of possible values PMF will generate. The numbers will be formatted using the mask you entered in the Decimal Format field.

   Data Sparsity

   Controls the amount of data PMF generates by letting you focus the data on dimensional choices:

   • None. Generates a Cartesian cross-product of all possible dimension values.
   • Dimensional Filters. You can specify filters for the dimension levels for the generated datapoint. To specify the filters, select this option and use the drop-down menus, as shown in the following image.

     

   • Train. You can base the dimension level values along which PMF generates data on another datapoint. This lets you keep a limited amount of data together.

     You can specify any datapoint to train from a loadable datapoint, user-entered datapoint, derived datapoint, or another generated datapoint.

   Recalculate all Derived Datapoints

   This option should remain enabled, unless you have a very large data mart and want to reserve recalculation for overnight or other offline processing.

   Note: This option enabled by default. To disable it, see Load Settings.

   Description

   A description of the datapoint.

7. Click Save. If minimum necessary entries are not set up to generate data, PMF will mark the generated datapoint as incomplete. Incomplete components do not participate in recalculation.

Tips:

• When generating random data for generated datapoints, PMF will wipe all existing data before regenerating it using your new rules. Generally, with generated datapoints, the Preview tab is useful for new data you are planning to generate into an empty datapoint, or when changing rules. If your rules have not changed, and data is showing up on Preview as 100% added and deleted, you should not regenerate data.
• If you are modeling a new metrics system without having any real data to work from, you will have no data loaded at all into PMF to train from. In this situation, set the Dimensional Filters option on your first generated datapoint, specify the dimensions, and have PMF generate the data. You can then train loading all of your generated datapoints to load based on that one datapoint.

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Generated Datapoints are primary sources for data in PMF. They are treated as first generation in any lineage, along with loadable datapoints and user-entered datapoints.

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Generated datapoints are used when you lack real data to prove that your model works, or they are needed for a demonstration. Once you are ready to use a working model with real data, generated datapoints are no longer necessary to feed your metrics.

To promote the generated datapoint:

• Add it to a loadable source so that its data can be harvested from an existing file, table, or view.
• Add it to a user-entered source, so that the data can be collected from end users.