Abstract: A database system comprising an interface and a processor may perform techniques described in this disclosure. The interface may receive a query for accessing a first dataset and a second dataset, where the query identifies multiple dimensions of the first dataset and the second dataset. The first dataset may include a first dimension, and the second dataset may include a second dimension, but excludes the first dimension. The processor may translate the query into a structured query that conforms to a database query language, where the structured query defines a join between the first dataset and the second dataset over the second dimension. The processor may also transmit the structured query to a database, and receive, responsive to the structured query, an indication that a virtual table was created that combines the first dataset and the second dataset over the second dimension of the second dataset.

Abstract: Techniques are described related to for generating/distributing state machines that are implemented within a security zone to obtain private information from one or more resources within the security zone. In various implementations, an automated assistant client implemented by processor(s) within the security zone may receive a free form natural language query (“FFNLQ”) that is answerable using private information available from resource(s) within the security zone. Data indicative of the FFNLQ may be provided to a semantic processor outside of the security zone, and the online semantic processor may return a state machine that is implemented by processor(s) within the security zone to obtain the private information from resource(s) within the security zone. Based on the state machine and the obtained private information, natural language output may be generated and presented to convey information responsive to the FFNLQ.

Abstract: Techniques are described related to for generating/distributing state machines that are implemented within a security zone to obtain private information from one or more resources within the security zone. In various implementations, an automated assistant client implemented by processor(s) within the security zone may receive a free form natural language query (“FFNLQ”) that is answerable using private information available from resource(s) within the security zone. Data indicative of the FFNLQ may be provided to a semantic processor outside of the security zone, and the online semantic processor may return a state machine that is implemented by processor(s) within the security zone to obtain the private information from resource(s) within the security zone. Based on the state machine and the obtained private information, natural language output may be generated and presented to convey information responsive to the FFNLQ.

Abstract: Techniques are described related to for generating/distributing state machines that are implemented within a security zone to obtain private information from one or more resources within the security zone. In various implementations, an automated assistant client implemented by processor(s) within the security zone may receive a free form natural language query (“FFNLQ”) that is answerable using private information available from resource(s) within the security zone. Data indicative of the FFNLQ may be provided to a semantic processor outside of the security zone, and the online semantic processor may return a state machine that is implemented by processor(s) within the security zone to obtain the private information from resource(s) within the security zone. Based on the state machine and the obtained private information, natural language output may be generated and presented to convey information responsive to the FFNLQ.

Abstract: Techniques are described related to for generating/distributing state machines that are implemented within a security zone to obtain private information from one or more resources within the security zone. In various implementations, an automated assistant client implemented by processor(s) within the security zone may receive a free form natural language query (“FFNLQ”) that is answerable using private information available from resource(s) within the security zone. Data indicative of the FFNLQ may be provided to a semantic processor outside of the security zone, and the online semantic processor may return a state machine that is implemented by processor(s) within the security zone to obtain the private information from resource(s) within the security zone. Based on the state machine and the obtained private information, natural language output may be generated and presented to convey information responsive to the FFNLQ.

Abstract: Systems, device and techniques are disclosed for publishing multiple versions of an application to an application market, via an application programming interface. The application programming interface may be configured to allow automated uploads of multiple version of an application without requiring individual uploads of each version. A developer associated party may provide the multiple versions of the application via the application programming interface. The multiple versions of the applications may be provided via the application programming interface and not an application market interface. The multiple versions may be published in an application to different set of users.