Abstract: Mechanisms are provided for performing cross-user dashboard behavior analysis and dashboard recommendation generation. Dashboard interfaces are presented to a user and the user inputs are tracked. Cognitive analysis of the user dashboard behavior pattern data is performed to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data. Cross-user correlation analysis operations are performed based on the user dashboard behavior pattern data and dashboard behavior pattern data of other users of a different user type to identify an intersection point. A recommendation output is generated and output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed, based on the identification of the intersection point and the determined reason for the user dashboard behavior.

Abstract: Mechanisms are provided for generating a dashboard recommendation based on tracked user input patterns and the operation of predictive analytics. The mechanisms present a dashboard interface to a user via a client computing device, and track user inputs to the client computing device at least during and after presentation of the dashboard interface to the user via the client computing device. The mechanisms apply predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, and correlate the predicted type of data with one or more portions of one or more other dashboard interfaces that provide a representation of data having a type matching the predicted type of data. The mechanisms output a recommendation output to the user via the client computing device recommending the user access the one or more other dashboard interfaces.

Abstract: Mechanisms are provided for performing cross-user dashboard behavior analysis and dashboard recommendation generation. Dashboard interfaces are presented to a user and the user inputs are tracked. Cognitive analysis of the user dashboard behavior pattern data is performed to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data. Cross-user correlation analysis operations are performed based on the user dashboard behavior pattern data and dashboard behavior pattern data of other users of a different user type to identify an intersection point. A recommendation output is generated and output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed. The recommendation is based on the identification of the intersection point and the determined reason for the user dashboard behavior.

Abstract: Mechanisms are provided for generating a dashboard recommendation based on tracked user input patterns and the operation of predictive analytics. The mechanisms present a dashboard interface to a user via a client computing device, and track user inputs to the client computing device at least during and after presentation of the dashboard interface to the user via the client computing device. The mechanisms apply predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, and correlate the predicted type of data with one or more portions of one or more other dashboard interfaces that provide a representation of data having a type matching the predicted type of data. The mechanisms output a recommendation output to the user via the client computing device recommending the user access the one or more other dashboard interfaces.

Abstract: Mechanisms are provided for performing cross-user dashboard behavior analysis and dashboard recommendation generation. Dashboard interfaces are presented to a user and the user inputs are tracked. Cognitive analysis of the user dashboard behavior pattern data is performed to determine a reason for user dashboard behavior represented by the user dashboard behavior pattern data. Cross-user correlation analysis operations are performed based on the user dashboard behavior pattern data and dashboard behavior pattern data of other users of a different user type to identify an intersection point. A recommendation output is generated and output that recommends at least one of a particular dashboard interface to access or a modification to the one or more dashboard interfaces to be performed, based on the identification of the intersection point and the determined reason for the user dashboard behavior.

Abstract: Mechanisms are provided for generating a dashboard recommendation based on tracked user input patterns and the operation of predictive analytics. The mechanisms present a dashboard interface to a user via a client computing device, and track user inputs to the client computing device at least during and after presentation of the dashboard interface to the user via the client computing device. The mechanisms apply predictive analytics to the tracked user inputs to predict a type of data the user is attempting to access, and correlate the predicted type of data with one or more portions of one or more other dashboard interfaces that provide a representation of data having a type matching the predicted type of data. The mechanisms output a recommendation output to the user via the client computing device recommending the user access the one or more other dashboard interfaces.