Data Flow rule form
Completing Data Flows

Source is the standard entry point of a data flow. A source defines data you read from through the data flow.

• A data flow contains one primary source. This primary source can originate from a data set, another data flow or represent an abstract set of results:

  • Abstract: results in the data class.

  • Data flow: the entry point is based on another data flow. This data flow can be under the same data flow class or another class.

    Only a data flow that has an abstract input can be used as the primary input in another data flow.

  • Data set: the entry point is based on the data defined in a data set in the data flow class.

  • Report definition: the entry point is based on a report definition. The scope of this report definition is the applies to class of the data flow. In order to distribute a data flow run across multiple nodes, select the Enable distributed run check box and specify the Partition Key.

    Note:  If want to use a report definition that does aggregation (generates database group by statement), create a property in the class on which the report is defined. The property must have the same name as the aggregate in the report definition.

• Secondary sources are added as you start combining data. Secondary data sources can originate from a data set, data flow, or report definition.

The compose pattern allows you to combine data from two sources into a page or page list property. This pattern requires a property to match data between the two sources. The starting data point is the shape where you start combining data from. Once the compose path is available, you define the secondary source by providing an existing data set or another data flow.

The convert pattern allows you to take data in one class and put it in another, overwriting identical properties (by name), or explicitly mapping properties that do not have the same name between source and target. This pattern requires a property to match data between the two sources. The data you pass is determined by the starting shape for the convert path. Once the convert path is available, you define its properties by selecting the target class and how to handle property mapping between source and target.

The mapping of properties between source and target can be handled automatically, or you explicitly define how to map source and target properties.

• Automatically means that properties in the source override properties with the same name in the target class.
• Explicit mapping allows you to manage the mapping in cases you want to control which properties result in an overwrite, or you do not have properties with the same name, although they are compatible properties that represent the same data.

The event strategy pattern allows you to reference and run an event strategy in a data flow.

Configuring the event strategy pattern

1. In the Results in field, specify the name of the class containing your data flow.
2. In the Event strategy field, specify the name of the event strategy you want to use.
3. In the Properties output mapping section, map the properties from your event strategy to the properties that exist in the class containing your data flow.
1. Click Add Item.
2. In the Set field, specify a target property that exists in the class containing your data flow.
3. In the equal to field, specify a source property.

The filter pattern allows you to specify filter conditions and apply them to each element of the input flow. The output flow consists of those elements which satisfy the filter conditions.

Configuring the filter pattern

1. In the Name field, specify a name for your filter.
2. In the Filter conditions section, click Add Item.

3. In the left field, specify the name of a property to be used by the filter.

4. From the dropdown list, select a comparison operator.
5. In the right field, specify a value for your filter condition.

This pattern allows you to reference an instance of the Free Text Model rule to deliver text analytics capabilities to users via data flows.

The merge pattern allows you to combine data in the primary and secondary data paths into a single track. This pattern requires the source to be in the same class, conditions to match between the primary and secondary sources, and how to handle data mismatches. The starting data point is the shape where you start merging data from. Once the merge path is available, you define the secondary source by providing an existing data set or another data flow.

In cases of no data match, you can select the option to exclude the source component results that do not match the merge condition. If one of the specified properties does not exist, the value of the other property is not included in the class that stores the merge results.

In cases of data mismatch, you can select which source is leading:

• Primary path: if there are properties with the same name but different values, merge takes the value in the primary source.
• Secondary path: if there are properties with the same name but different values, merge takes the value in the secondary source.

The strategy pattern allows you to run a strategy based on combined or not combined data. The data you pass to execute the strategy is determined by the starting shape for the strategy path. Once the strategy path is available, you define which strategy to run and, if applicable, model learning and outcome capture settings.

Model learning and outcome capture settings are related, but exclusive. In a data flow, each strategy can only run in one context: storing adaptive inputs and strategy results, or retrieving them.

• Store adaptive inputs and strategy results for: used for delayed adaptive model learning and issuing response capture at a later date, and applies when the strategy executed by the data flow is designed to issue a decision. Provide an amount of time in days, hours or minutes for persisting the inputs passed to adaptive models and strategy results, assuming a strategy that issues a decision.
• Retrieve previous inputs and results using key: used to retrieve the adaptive inputs and strategy results, and applies when the strategy executed by the data flow is designed to capture results.

By default, the strategy results are stored in the SR class. If you need, you can change it and select the input class of the strategy pattern as the output class. You can also specify any other class where you want to store your strategy results.

Note: When you decide to change the output class, map the properties from the SR class to the properties of the class you select.

The destination pattern defines the data point you write to and it is the standard output point of a data flow. Every data flow defines one or more destinations that output all results, or results based on a given condition.

The types of data representation you can use in a destination are: Abstract, Activity, Case, Data flow, and Data set.

The type of data set determines the write operation performed by the destination: database tables, decision data stores, ADM by using the pxAdaptiveAnalytics data set, IH by using the pxInteractionHistory data set, or a VBD data source.

When your data set is a database table, you need to specify one of the save options:

• Only insert new records
• Insert new and overwrite existing records