Abstract: Computing systems, methods, and non-transitory computer-readable storage media are provided. An average percentage of identifier collisions are determined in historical event data based on short identifiers associated with a first event location or a second event location geographically dispersed from each other. Behavioral models are generated based on behavior analytics of short identifiers in the historical event data. Adjusted behavioral models are generated based on the average percentage of identifier collisions. A client device obtains a short identifier and an adjusted behavioral model is determined. Whether the short identifier is associated with a previous event of a first event type at the first event location is determined. A new event value or an intermediate event value for the first event type is calculated. The client device is notified using the new event value or the intermediate event value, whereby the client device displays a notification.

Abstract: Embodiments relate to computing systems, methods, and non-transitory computer-readable storage media. Identifier collisions are determined in historical event data based on short identifiers associated with a first event location and short identifiers associated with a second event location geographically dispersed from the first event location. Behavior analytics are performed on short identifiers in the historical event data to generate behavioral models associated with the short identifiers. Adjusted behavioral models are generated based on the determined identifier collisions. A short identifier is obtained from a client device, and based on the short identifier, an adjusted behavioral model of the adjusted behavioral models is determined. An expected event frequency is determined based on the adjusted behavioral model and an actual event frequency is determined.

Abstract: Embodiments relate to systems, devices, non-transitory computer-readable storage media, and computer-implemented methods for performing behavioral analytics using short identifiers by obtaining historical event data for multiple events and multiple short identifiers, where each event is associated with a short identifier, selecting a first event location associated with a first event, selecting a second event location associated with a second event that is geographically dispersed from the first event location, determining identifier collisions based on short identifiers associated with the first event location and short identifiers associated with the second event location in the historical event data, determining an average percentage of identifiers collisions based on the identifier collisions that were determined, performing behavior analytics on the short identifiers to generate behavioral models associated with the short identifiers, and generating adjusted behavioral models based on the average percent