Abstract: Embodiments are directed towards managing data. Attributes of model fields of a plurality of model objects may be analyzed. If the analysis of the attributes discovers primary key fields in the model objects, the characteristics of the primary keys may be compared with the characteristics of other model objects. If affirmative results of the comparison indicate that one or more foreign key fields may be in the other model objects, one or more relationships that associate the primary key fields with the foreign key fields may be provided. And, a system model may be provided based on the relationships and the model objects that include the primary key fields and the model objects that include the foreign key fields.

Abstract: Embodiments are directed towards managing data using modeling platform. A principal data set and match data set may be provided. The principal data set may include principal objects and the match data set may include other model objects. Blocking may associate each of the plurality of principal objects with potential match objects. Principal objects from the plurality of principal objects may be selected based on heuristics, their associated potential match objects, or the like. Potential match objects may be labeled as a true match or a non-match with respect to their associated principal object. A ranker may be trained to identify top-ranked matches based on the labeled potential match objects. The trained ranker may be employed to indicate matches where each matching other model object is a potential match object that is ranked higher than other potential match objects that are associated with its matched principal object.

Abstract: Embodiments are directed towards managing data. An attributes engine may be employed to perform various actions, including: analyzing characteristics of model object features of a plurality of model objects; classifying the model object features based on the characteristics, such that the characteristics include a data type and values of the model object features; and associating similarity tasks with the model object features based on their classification. A similarity engine may then be employed to perform further actions, including: providing a similarity model that includes the similarity tasks; employing the similarity model to provide candidate similarity scores based on exemplar model objects labeled as being similar; modifying the similarity model based on the exemplar model objects and the candidate similarity scores; employing the modified similarity model to provide similarity scores for model objects based on the one or more similarity tasks.

Abstract: Embodiments are directed towards managing data. Attributes of model fields of a plurality of model objects may be analyzed. If the analysis of the attributes discovers primary key fields in the model objects, the characteristics of the primary keys may be compared with the characteristics of other model objects. If affirmative results of the comparison indicate that one or more foreign key fields may be in the other model objects, one or more relationships that associate the primary key fields with the foreign key fields may be provided. And, a system model may be provided based on the relationships and the model objects that include the primary key fields and the model objects that include the foreign key fields.

Abstract: Embodiments are directed towards managing data using modeling platform. A principal data set and match data set may be provided. The principal data set may include principal objects and the match data set may include other model objects. Blocking may associate each of the plurality of principal objects with potential match objects. Principal objects from the plurality of principal objects may be selected based on heuristics, their associated potential match objects, or the like. Potential match objects may be labeled as a true match or a non-match with respect to their associated principal object. A ranker may be trained to identify top-ranked matches based on the labeled potential match objects. The trained ranker may be employed to indicate matches where each matching other model object is a potential match object that is ranked higher than other potential match objects that are associated with its matched principal object.

Abstract: Embodiments are directed towards managing data using modeling platform. A principal data set and match data set may be provided. The principal data set may include principal objects and the match data set may include other model objects. Blocking may associate each of the plurality of principal objects with potential match objects. Principal objects from the plurality of principal objects may be selected based on heuristics, their associated potential match objects, or the like. Potential match objects may be labeled as a true match or a non-match with respect to their associated principal object. A ranker may be trained to identify top-ranked matches based on the labeled potential match objects. The trained ranker may be employed to indicate matches where each matching other model object is a potential match object that is ranked higher than other potential match objects that are associated with its matched principal object.

Abstract: Embodiments are directed towards managing data using modeling platform. A principal data set and match data set may be provided. The principal data set may include principal objects and the match data set may include other model objects. Blocking may associate each of the plurality of principal objects with potential match objects. Principal objects from the plurality of principal objects may be selected based on heuristics, their associated potential match objects, or the like. Potential match objects may be labeled as a true match or a non-match with respect to their associated principal object. A ranker may be trained to identify top-ranked matches based on the labeled potential match objects. The trained ranker may be employed to indicate matches where each matching other model object is a potential match object that is ranked higher than other potential match objects that are associated with its matched principal object.

Abstract: Architecture that employs dynamic assertion providers in logic-based security policy languages. The dynamic assertion providers provide a mechanism by which data can be processed in an efficient, goal-directed manner. The application of dynamic assertion providers enables the inferencing over type hierarchies, including hierarchies of personally identifiable information and also for file systems which are hierarchal in nature. When searching for facts to bind against for a given subgoal, dynamic assertion providers are also queried. These objects follow an interface which can implement an arbitrary method to provide facts to the logic engine in a goal-directed, on-demand manner. Hierarchical types can be implemented using a hierarchy provider.

Abstract: Architecture that employs dynamic assertion providers in logic-based security policy languages. The dynamic assertion providers provide a mechanism by which data can be processed in an efficient, goal-directed manner. The application of dynamic assertion providers enables the inferencing over type hierarchies, including hierarchies of personally identifiable information and also for file systems which are hierarchal in nature. When searching for facts to bind against for a given subgoal, dynamic assertion providers are also queried. These objects follow an interface which can implement an arbitrary method to provide facts to the logic engine in a goal-directed, on-demand manner. Hierarchical types can be implemented using a hierarchy provider.