Oracle International Corporation

Alan Waxman

Reza M B'Far

Ryan Golden

Uppili Srinivasan

Yasin Cengiz

Tsai-Ming Tseng

Nigel Jacobs

Sreedhar Chitullapally

Kent Arthur Spaulding

Mike Adourian

Siddharth Sinha

Elizabeth Lingg

Huyvu Nguyen

Fei Wihardjo

Christopher Leone

Steven Miranda

Joseph Mazur

Heuristic caching techniques are used to improve application usability within the constraints of computing resources. A user's behavior in connection with an application is recorded and used to intelligently determine which transitions of a user interface of the application to cache. In addition, the user may have one or more characteristics. Such characteristics may also be used to identify other users so that the other users' behavior in connection with the application can be used for intelligently determining which transitions of the user interface to cache.

Heuristic caching to personalize applications

Techniques, including systems are methods, for analyzing data from multiple sources are disclosed and suggested herein. In an embodiment, information from one or more information sources is received. The received information is stored in one or more data stores that collectively implement one or more ontologies. One or more conditions are applied to the information in the one or more data stores to determine a conclusion.

Techniques for analyzing data from multiple sources

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.

Techniques for extracting semantic data stores

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.

Generic ontology based semantic business policy engine

Techniques, including systems are methods, for analyzing data from multiple sources are disclosed and suggested herein. In an embodiment, external information from one or more external information sources and internal information from one or more internal information sources is received. The received external information and internal information are stored in one or more data stores that collectively implement one or more ontologies. One or more conditions are applied to the external information and internal information in the one or more data stores to determine a conclusion and the conclusion is provided to a user.

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.

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Morganville, NJ, United States of America

Alan Waxman