Abstract: In an approach to a passively managed loyalty program using customer images and customer behaviors, one or more processors may receive one or more customer characteristics and one or more customer behaviors. One or more processors may also query for a first user based on the one or more customer characteristics and the one or more customer behaviors. One or more processors may further receive a user profile ID based on the query. One or more processors may additionally store at least one customer characteristic from the one or more customer characteristics and at least one customer behavior from the one or more customer behaviors under the user profile ID.

Abstract: A source site includes a controller, a set of source worker nodes, and a message queue connected between the controller and source worker nodes. A destination site includes a set of destination worker nodes. The controller identifies differences between a first snapshot created at the source site at a first time and a second snapshot created at a second time, after the first time. Based on the differences, a set of tasks are generated. The tasks include one or more of copying an object from the source to destination or deleting an object from the destination. The controller places the tasks onto the message queue. A first source worker node retrieves the first task and coordinates with a first destination worker node to perform the first task. A second source worker nodes retrieves the second task and coordinates with a second destination worker node to perform the second task.

Abstract: Systems, methods, and other embodiments associated with grouping data using data streams are described. In one embodiment, a method includes publishing data into a data stream. The example method may also include evaluates phrases within data in the data stream to identify a set of features having data divergence amongst the data above a divergence threshold. The example method may also include computing a model correlating data to the set of features. The example method may also include applying the model to data to compute feature vectors for the data. The example method may also include comparing the feature vectors to identify and group similar data.