Applications, Aggregates & Enrichments

All of the feed applications configured in HCL Detect can be seen by clicking on the Administration link in the top navigation bar and then by clicking the Settings menu item. We have to use Feed Management tab to see feed applications.

This dashboard shows the applications and their associated data model, e.g., ‘Topup', ‘Usage', etc.

Users with the appropriate permissions can use this page to view, edit, add and/or delete the aggregates and enrichments associated with a given feed application.

The existing aggregates configured for a feed application can be seen by clicking on the ShowAll button in the Aggregations row for that application.

The dialog showing the aggregates associated with the TopupDemo application is below:

The aggregates associated with a feed can be edited and removed.

To explain the aggregates a bit further, let us consider the aggregate called topupAmountBySubscriber. The dialog that opens up upon clicking on the edit button is shown below:

This aggregate is designed to count the number of calls by subscriber over various time windows (e.g., currentDay, lastDay, etc.).

topupAmountBySubscriber, i.e. the aggregate Name, is the name that is used to refer to the aggregate when using it in a trigger.

The Aggregationtype, in this case Sum, is used to accumulate the topup amounts, and the group-by attribute, in this case, MSISDN, is used to produce per-user tallies, since it identifies individual subscribers.

Additional group-by attributes can be added in a manner similar to the group-by clause used in traditional relational database management system (RDBMS) query languages.

A filter condition can also be used when only a subset of tuples is to be considered for an aggregation.

For instance, if the incoming tuples in the feed contain a Boolean isDroppedCall, indicating a dropped call, then one can calculate droppedCallsBySubscriber by using isDroppedCall equals to True as a filter condition with the MSISDN as the group-by attribute and Count as the aggregation type.

To add a new aggregate, we need to click on Add button for that feed application. A dialog with form needs to be filled by filling Aggregate Name, Aggregation Type, Attribute to Aggregate, Group by Attribute and Aggregation Filter:

The list of enrichments associated with a feed can be seen by clicking on the Show button in the Enrichments row. The enrichments associated with the Topup Demo feed can be seen below:

HCL Detect supports 4 kinds of enrichments:

• The transformed attributes enrichment enables one to add attributes to the tuple that have either a constant value or a value that can easily be derived from an UEL expression. Such transformed attribute may be needed by other enrichments or retained as an attribute in the enriched tuple. Note that retaining and forwarding the constant attributes as part of the output tuple from the enrichment step is optional.
• The lookup-based enrichment, which retrieves attributes from the profile store and possibly add them to the outgoing tuple. Note that retaining and forwarding the looked up attributes as part of the output tuple from the enrichment step is optional. We have to first select a key attribute present in the tuple, the associate Pinpoint table will be listed in Lookup Table drop-down, finally we have to select the attribute from the lookup table to be looked up.
• The aggregate-based enrichment, which fetches data from FastPast and possibly adds such attributes to the outgoing tuple. As part of configuring an aggregated attribute, it is necessary to (1) select an aggregate from the list of available aggregates for the feed, (2) select the window length unit, which can be Minute, Hour, Day, Month or Year, (3) select the period which can be one of Current or Last and (4) optionally select Window length, which can be used to indicate that only a subset of an aggregate is to be used. For instance, when the Day window length unit and the Last period with Window Length set to 7 is chosen, the aggregated value for the last 7 days can be computed. Note that when using the Minute window length unit, Window Length needs to be multiple of 10-minute i.e., backend configured sub-hour buckets length. Similarly to lookup-based enrichment, aggregate-based enrichment attributes may or may not be retained and forwarded as part of the output tuple.
• The derived attributes enrichment, which enables the execution of an external Python function. One such a function takes in a tuple (as well as any other required additional parameters, if any), performs a user-defined computation, and produces a result. This function invocation's return value can then be retained and forwarded as part of an outgoing tuple. This capability can be used, for instance, to invoke a scoring function on an incoming tuple, which computes a score (e.g., based on previously mined data) that can then be added as an attribute to the corresponding outgoing tuple. The derived attributes enrichment are basically of three types, i.e., enrichment function based, expression based and scorer function based. expression based takes an UEL expression explained in Miscellaneous » UEL section.

The interface for editing enrichments is shown below:

The interface for editing derived attribute type enrichments is shown below:

Enrichment operations can be configured to before aggregations are performed or after aggregations are performed. The value of a enriched aggreate-based attribute may change based on whether it configured to run before or after the aggregation operation. We can choose this while creating an enrichment by checking the Post-Aggregation Enrichment check-bit as shown in the below image: