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Adding aggregations in event strategies

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By adding aggregations, you can define various functions to apply to events in an event strategy. For example, you can sum property values from incoming events for trend detection, such as the number of dropped calls, transactions, or aggregated credit card purchases.

  1. In an event strategy, add an Aggregate shape.

  2. Right-click the Aggregate shape to open the Aggregate properties window.

  3. Click Add aggregation.

  4. Select a function to use for aggregating data.

    For more information, see Aggregation options in event strategies.
  5. In the Source field, specify a property that is available in the data flow.

    By default, all properties defined in the event strategy class are available through a stream data set. This makes properties of the event strategy class available to the Real-time Data stream shape. Depending on how the event strategy is constructed, additional properties can be available in the list of the aggregate source field. Additional properties can include:
    • Properties from aggregations within the same event strategy processed prior to the current one.

    • Properties that are coming from a preceding Lookup shape.

    • Properties that are coming from mapped fields from the preceding Join shapes.

  6. In the Aggregate field, provide a name.

    The name is a dynamic property that exists only in the context of the event strategy. The dynamic property contains the result of the aggregation function. Aggregation names within an event strategy must be unique.
  7. Optional:

    To add more aggregations, repeat steps 3 through 6.

  8. Click Submit.

  • Aggregation options in event strategies

    You can aggregate values of event properties to derive such insights as sum, count, standard deviation, or median. By looking at the aggregated data, you can detect meaningful patterns that can help you optimize your next-best-action offering.

  • Approximate median calculation

    Use the approximate median to calculate the center value of a data group in which strong outliers might distort the outcome.

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