Abstract: A computing device accesses a session log that includes a recording of user interactions of a user during a session with an application instance in a computing environment. The computing device cleanses the session log to remove a portion of the content included in the session log to generate a cleansed session log and converts the cleansed session log into a session vector representation using a finite dictionary built from a plurality of session logs associated with a plurality of users that have interacted with the computing environment. The computing device generates a user model for the user using the session vector representation and a plurality of other session vector representations associated with the user. The model may be used to perform management and security operations in the computing environment.

Abstract: A computing device accesses a session log that includes a recording of user interactions of a user during a session with an application instance in a computing environment. The computing device cleanses the session log to remove a portion of the content included in the session log to generate a cleansed session log and converts the cleansed session log into a session vector representation using a finite dictionary built from a plurality of session logs associated with a plurality of users that have interacted with the computing environment. The computing device generates a user model for the user using the session vector representation and a plurality of other session vector representations associated with the user. The model may be used to perform management and security operations in the computing environment.

Abstract: There are provided systems and methods for dynamic autoscaling of server resources using intelligent demand analytic systems. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing resource to provide computing resources to users. During use of such computing resources by end users and their computing devices, different demand and needs may be required by such users and devices. The service provider may utilize an intelligent machine learning system to predict computing resource needs and demands at different future time periods based on past usages over similar time periods, computing requests and demands, and network communications. The machine learning engines may identify one or more usages curves, which may be of one or more degrees of curvature, to determine potential future usage. Using these past analytics, the service provider may dynamically scale automatic provision of computing resources.