1. A method comprising: receiving a dataset in a tabular form, the dataset having a plurality of rows and a plurality of columns; processing the dataset to generate indices representing probabilistic relationships between the rows and the columns of the dataset, wherein the processing includes learning a joint probability distribution over the dataset to identify and describe the probabilistic relationship between elements of the dataset, the learning being controlled by a default set of configuration parameters; storing the indices in a database; exposing an Application Programming Interface (API) to query the indices in the database system; receiving a request for a predictive query or a latent structure query against the indices in the database system, wherein the request includes at least one specified column as an input parameter with the predictive query or the latent structure query; querying the database system for a predictive result set or a latent structure result responsive to the request for the predictive query or the latent structure query respectively, the querying submitted to the database system via the API using the predictive query or the latent structure query and the at least one specified column as the input parameter to the querying; and returning the predictive result set or a latent structure result responsive to the request, wherein a confidence indicator is returned with the predictive result set or a latent structure result, the confidence indicator indicating whether a predictive relationship or a latent relationship exist between the at least one specified column and elements of the predictive result set or a latent structure result.
9. The method of claim 1 , wherein processing the dataset comprises at least one of: learning a Dirichlet Process Mixture Model (DPMM) of the dataset; learning a cross categorization of the dataset; learning an Indian buffet process model of the dataset; and learning a mixture model or a mixture of finite mixtures model of the dataset.