Abstract: The present disclosure pertains to processing a data model having hierarchical data. A front-end computer sends a request to a back-end computer for dimension candidates for the data model, which is visualized by the front-end computer. The front-end computer is configured score and rank such dimension candidates in order to determine statistics from the data model. The back-end computer determines dimension candidates based on their cardinality and hierarchical information and sends the dimension candidates to the front-end computer. The front-end computer performs the scoring and ranking of dimension candidates and determines statistics for a set of the dimension candidates. The statistics may be presented to a user along with charts and graphs representing the data model.

Abstract: The present disclosure pertains to processing a data model having hierarchical data. A front-end computer sends a request to a back-end computer for dimension candidates for the data model, which is visualized by the front-end computer. The front-end computer is configured score and rank such dimension candidates in order to determine statistics from the data model. The back-end computer determines dimension candidates based on their cardinality and hierarchical information and sends the dimension candidates to the front-end computer. The front-end computer performs the scoring and ranking of dimension candidates and determines statistics for a set of the dimension candidates. The statistics may be presented to a user along with charts and graphs representing the data model.

Abstract: Techniques are described for integrating predictive models into applications, to enable the applications to provide predictive functionality. Using the framework according to implementations, predictive models and their supporting libraries may be incorporated into applications without requiring application developers to be knowledgeable regarding the particular features of the predictive models and/or libraries. The framework exposes a common and consistent application programming interface (API) on top of the predictive libraries. Applications can use the API to interact with the predictive models, thus enabling the applications to leverage predictive functionality. Implementations also provide an API which may be used by applications to request the retraining of predictive models.