Abstract: Techniques for implementing policies. In an embodiment, first data is stored in a first data store according to a first schema. A second schema is defined based at least in part on a policy and an ontology. Second data, which includes at least a portion of the first data, is stored in a second data store according to the second schema. Storing the second data is based at least in part on a mapping of the first schema to the second schema. At least a portion of the second data is analyzed and results of the analysis are provided to a user.

Abstract: Techniques for storing information include making determinations whether to store data from a data source based at least in part on one or more reputation metrics calculated for an individual associated with the information. The scope of information collected about an individual is varied based on the individual's reputation and/or the reliability of the source of information, which may be a social network system or other data source.

Abstract: Reputation metrics are used to gauge risk of individuals to an organization, such as employees of a business. The reputation metrics may be calculated from both internal and external data sources, including social network profiles of the individuals. Calculations of risk are used to make determinations regarding the activities the individuals are authorized to engage in.

Abstract: User profiles can be analyzed to identify profiles matching to the same identity. For example, profiles from different social network systems are analyzed to determine if the profiles are associated with the same user of the social network systems. Multiple heuristics may be calculated using different algorithms. The calculated heuristics may then be combined to generate a match score that indicates whether two profiles match.

Abstract: Techniques for implementing policies. In an embodiment, first data is stored in a first data store according to a first schema. A second schema is defined based at least in part on a policy and an ontology. Second data, which includes at least a portion of the first data, is stored in a second data store according to the second schema. Storing the second data is based at least in part on a mapping of the first schema to the second schema. At least a portion of the second data is analyzed and results of the analysis are provided to a user.

Abstract: Techniques for reasoning data are disclosed. The techniques may include storing data in an ontology. The data may be partitioned and/or distributed to a plurality of reasoners. Processed data from the plurality of reasoners may then be combined, the processed data having been received from the plurality of reasoners and/or having been processed by the plurality of reasoners.

Abstract: Techniques for reasoning data are disclosed. The techniques may include storing data in a data store which may be a semantic data store. A plurality of reasoning modules are used to reason the data in order to detect violations of one or more business policies. The reasoning modules may include semantic reasoning modules and/or pattern-based reasoning modules. Results of reasoning the data may be displayed to one or more users.

Abstract: Techniques for processing ontologies include a computer-implemented method of processing an ontology. The method includes storing data in an ontology and partitioning the ontology into a plurality of partitions. The partitions are distributed among a plurality of reasoner instances. The reasoner instances are directed to reason the partitions and the results from the reasoner instances are combined. If data is changed, an inference library resulting from the combined results may be updated by updating only those portions of the inference library affected by the changed data.

Abstract: Techniques, including systems and methods, for generating data are disclosed and suggested herein. Original data used in connection with one or more applications is analyzed in order to determine one or more distribution characteristics for the original data. The distribution characteristics are used to generate data that is similarly distributed. The generated data may be used as seed data for demonstrating, testing, or otherwise using one or more applications.

Abstract: Techniques for displaying information. Policy violations are identified, based at least in part on data stored in a data store. For the policy violations, a plurality of semantic objects related to the violations are identified. Arrangements of graphical objects are displayed where the graphical objects represent the identified semantic objects and where the arrangement indicates one or more relationships between pairs of the semantic objects.

Abstract: Techniques for reasoning data are disclosed. The techniques may include storing data in a data store which may be a semantic data store. A plurality of reasoning modules are used to reason the data in order to detect violations of one or more business policies. The reasoning modules may include semantic reasoning modules and/or pattern-based reasoning modules. Results of reasoning the data may be displayed to one or more users.

Abstract: Techniques for enforcing policies. A set of data is stored in one or more data stores. A plurality of semantic concepts for an ontology are defined and a map is constructed from the set of data to the concepts. The map is executed in order to transform data from the set of data to a second set of data. The second set of data is stored according to the ontology. The second set of data is reasoned in order to determine compliance with one or more policies.

Abstract: Techniques for processing ontologies include a computer-implemented method of processing an ontology. The method includes storing data in an ontology and partitioning the ontology into a plurality of partitions. The partitions are distributed among a plurality of reasoner instances. The reasoner instances are directed to reason the partitions and the results from the reasoner instances are combined. If data is changed, an inference library resulting from the combined results may be updated by updating only those portions of the inference library affected by the changed data.

Abstract: Embodiments of the present invention relate to techniques for creating policies. A plurality of objects representative of semantic objects are provided to a user. An arrangement of a subset of the objects, the arrangement representative of a policy, is received. The arrangement is converted to instructions for implementation by an application configured to implement policies. One or more of the objects may include fields and/or controls for specifying criteria of semantic objects represented by the objects.

Abstract: Techniques for implementing policies. In an embodiment, first data is stored in a first data store according to a first schema. A second schema is defined based at least in part on a policy and an ontology. Second data, which includes at least a portion of the first data, is stored in a second data store according to the second schema. Storing the second data is based at least in part on a mapping of the first schema to the second schema. At least a portion of the second data is analyzed and results of the analysis are provided to a user.