Abstract: Techniques for generating a report for a website are presented herein. A method can include accessing, by one or more computing devices, a plurality of unidentified events. Each event in the plurality of unidentified events can be associated with one or more properties. Additionally, the method can calculate, using a machine-learned prediction model, a number of pseudo users associated with the plurality of unidentified events based on an event-to-user-ratio and a total number of unidentified events. Moreover, the method can include assigning a first event from the plurality of unidentified events to a first pseudo user based on the one or more properties of the first event. Furthermore, the method can include generating the report for the website. The report includes information derived from the first event being assigned to the first pseudo user.

Abstract: Techniques for generating a report for a website are presented herein. A method can include accessing, by one or more computing devices, a plurality of unidentified events. Each event in the plurality of unidentified events can be associated with one or more properties. Additionally, the method can calculate, using a machine-learned prediction model, a number of pseudo users associated with the plurality of unidentified events based on an event-to-user-ratio and a total number of unidentified events. Moreover, the method can include assigning a first event from the plurality of unidentified events to a first pseudo user based on the one or more properties of the first event. Furthermore, the method can include generating the report for the website. The report includes information derived from the first event being assigned to the first pseudo user.

Abstract: Techniques for generating a report for a website are presented herein. A method can include accessing, by one or more computing devices, a plurality of unidentified events. Each event in the plurality of unidentified events can be associated with one or more properties. Additionally, the method can calculate, using a machine-learned prediction model, a number of pseudo users associated with the plurality of unidentified events based on an event-to-user-ratio and a total number of unidentified events. Moreover, the method can include assigning a first event from the plurality of unidentified events to a first pseudo user based on the one or more properties of the first event. Furthermore, the method can include generating the report for the website. The report includes information derived from the first event being assigned to the first pseudo user.

Abstract: A method, non-transitory computer readable medium, and apparatus for predicting a location behavior of at least one individual are disclosed. For example, the method receives a plurality of social networking messages having spatial location data and user identification information, filters the plurality of social networking messages to remove one or more of the plurality of social networking messages that are not related to mobility of a user to create a filtered plurality of social networking messages, creates a population model by applying a kernel density estimation to the filtered plurality of social networking messages, creates an individual model for each different user identification by applying the kernel density estimation to a subset of the filtered plurality of social networking messages for the each different user identification and generates a probability density function map that predicts the location behavior of the at least one individual.