Abstract: A method and system for presenting a promotional offer based on detected data. Data that is generated by at least one device detecting one or more physical actions performed by a person is obtained. The device is located in a same physical environment as the person. The data is analyzed to determine that a probability of an undesired action, by the person, is above a threshold value. Responsive to determining that the probability of the undesired action is above the threshold value, a promotional offer is presented to the person. The promotional offer lowers the probability of the undesired action by the person.

Abstract: A method and system for presenting a promotional offer based on detected data. Data that is generated by at least one device detecting one or more physical actions performed by a person is obtained. The device is located in a same physical environment as the person. The data is analyzed to determine that a probability of an undesired action, by the person, is above a threshold value. Responsive to determining that the probability of the undesired action is above the threshold value, a promotional offer is presented to the person. The promotional offer lowers the probability of the undesired action by the person.

Abstract: A database system of a mobile device that includes an on-device database service is provided. The on-device database service accepts a connection request from an application, and receives a database command from the application. The on-device database service translates the database command into a structured query language command, and transmits the structured query language command to an embedded database. After a database operation is performed by the embedded database, the on-device database service receives a result of the structured query language command that is transmitted from the embedded database, and the on-device database service transmits the result to the application.

Abstract: A database system of a mobile device that includes an on-device database service is provided. The on-device database service accepts a connection request from an application, and receives a database command from the application. The on-device database service translates the database command into a structured query language command, and transmits the structured query language command to an embedded database. After a database operation is performed by the embedded database, the on-device database service receives a result of the structured query language command that is transmitted from the embedded database, and the on-device database service transmits the result to the application.

Abstract: Methods and systems for platform-independent selective ahead-of-time compilation are herein described. A method selector comprising a profiling tool and heuristic selects a subset of methods for ahead-of-time compilation. The profiling tool ranks a set of methods according to predetermined criteria, and the heuristic identifies the subset of methods from the set of methods. An ahead-of-time compiler comprises a first unit and a second unit. The first unit converts, for each selected method, bytecodes corresponding to the selected method to a platform-independent intermediate representation. The second unit optimizes the platform-independent intermediate representation of each selected method, wherein each optimized intermediate representation is stored with a corresponding selected method. A virtual machine on a device converts an optimized intermediate representation associated with a selected method loaded onto the device to platform-dependent machine code.

Abstract: Methods and systems for platform-independent selective ahead-of-time compilation are herein described. A method selector comprising a profiling tool and heuristic selects a subset of methods for ahead-of-time compilation. The profiling tool ranks a set of methods according to predetermined criteria, and the heuristic identifies the subset of methods from the set of methods. An ahead-of-time compiler comprises a first unit and a second unit. The first unit converts, for each selected method, bytecodes corresponding to the selected method to a platform-independent intermediate representation. The second unit optimizes the platform-independent intermediate representation of each selected method, wherein each optimized intermediate representation is stored with a corresponding selected method. A virtual machine on a device converts an optimized intermediate representation associated with a selected method loaded onto the device to platform-dependent machine code.