Abstract: This document relates to compilation of source code into services. One example method involves receiving input source code, identifying data dependencies in the input source code, and identifying immutability points in the input source code based at least on the data dependencies. The example method also involves converting at least some of the input source code occurring after the immutability points to one or more service modules.

Abstract: This document relates to compilation of source code into services. One example method involves receiving input source code, identifying data dependencies in the input source code, and identifying immutability points in the input source code based at least on the data dependencies. The example method also involves converting at least some of the input source code occurring after the immutability points to one or more service modules.

Abstract: This document relates to orchestration and scheduling of services. One example method involves obtaining dependency information for an application. The dependency information can represent data dependencies between individual services of the application. The example method can also involve identifying runtime characteristics of the individual services and performing automated orchestration of the individual services into one or more application processes based at least on the dependency information and the runtime characteristics.

Abstract: This document relates to orchestration and scheduling of services. One example method involves obtaining dependency information for an application. The dependency information can represent data dependencies between individual services of the application. The example method can also involve identifying runtime characteristics of the individual services and performing automated orchestration of the individual services into one or more application processes based at least on the dependency information and the runtime characteristics.

Abstract: A technique is described herein that intelligently deploys resources in a data center for a new program. The new program has, at least in part, unknown runtime characteristics. The technique involves collecting plural input factors that provide evidence of an expected runtime behavior of the new program. It does so by identifying at least one related program that differs from the new program, but has a specified degree of relatedness to the new program. The collecting operation then obtains information that describes an amount of resources that the related program(s) consume when run. Based on at least some of the plural input factors, the technique generates an estimated amount of resources that the new program is expected to consume when it is run. The technique then determines and deploys a configuration of resources in the data center that will provide the estimated amount of resources.

Abstract: A technique is described herein that intelligently deploys resources in a data center for a new program. The new program has, at least in part, unknown runtime characteristics. The technique involves collecting plural input factors that provide evidence of an expected runtime behavior of the new program. It does so by identifying at least one related program that differs from the new program, but has a specified degree of relatedness to the new program. The collecting operation then obtains information that describes an amount of resources that the related program(s) consume when run. Based on at least some of the plural input factors, the technique generates an estimated amount of resources that the new program is expected to consume when it is run. The technique then determines and deploys a configuration of resources in the data center that will provide the estimated amount of resources.

Abstract: A technique is described herein for updating a running application that includes a plurality of program modules (e.g., services). The technique performs its updating operation without having to suspend the execution of the running application, and without reloading all of the program modules in the running application. The technique leverages a mapping component to map a calling program module's call to a function to a called program module that implements the function. A current application manifest provides mapping logic for use by the mapping component. In some examples, the technique also transforms data passed by the calling program module to conform to a data format expected by the called program module. This is appropriate when the calling program module and the called program module use different schemas to define the data.

Abstract: This document relates to orchestration and scheduling of services. One example method involves obtaining dependency information for an application. The dependency information can represent data dependencies between individual services of the application. The example method can also involve identifying runtime characteristics of the individual services and performing automated orchestration of the individual services into one or more application processes based at least on the dependency information and the runtime characteristics.

Abstract: This document relates to compilation of source code into services. One example method involves receiving input source code, identifying data dependencies in the input source code, and identifying immutability points in the input source code based at least on the data dependencies. The example method also involves converting at least some of the input source code occurring after the immutability points to one or more service modules.

Abstract: The modifying of a natural language interpretation model for interpreting natural language queries. The system discovers modifications that one or more queriers made to one or more original query results of one or more natural language queries to generate one or more modified query results. The system then uses the discoveries to identify one or more changes to a natural language interpretation model that would result (given the same natural language queries) in one or more query results that more accurately reflect the one or more modified query results. The system the causes the natural language interpretation model to be modified with at least one of the one or more identified changes. Accordingly, over time, the natural language interpretation model may learn from observations of its own performance.

Abstract: Presenting data from different data providers in a correlated fashion. A first query is performed on a first data set controlled by a first entity to capture a first set of data results. Then a second query is performed on a second data set controlled by a second entity to capture a second set of data results. A relationship ontology that correlates data stored in different data stores controlled by different entities is then consulted to identify one or more relationships between data in the selected results set and the second data set.

Abstract: Mechanisms to help a computing system respond to a request for information within a data model. After determining that there is insufficient information within the data model to respond to the request, the computing system identifies one or more additional data sources that are external to the data model and that contain information suitable to respond to the request. The computing system then automatically supplements the data model with at least one of such additional data sources. The computing system then responds to the request using the supplemented data model. In some embodiments, the supplementation is performed in advance of the request by analyzing the characteristics of the data model and/or by anticipating possible future requests. Thus, a data model grows automatically in response to particular usage of that data model to satisfy requests.

Abstract: The modifying of a natural language interpretation model for interpreting natural language queries. The system discovers modifications that one or more queriers made to one or more original query results of one or more natural language queries to generate one or more modified query results. The system then uses the discoveries to identify one or more changes to a natural language interpretation model that would result (given the same natural language queries) in one or more query results that more accurately reflect the one or more modified query results. The system the causes the natural language interpretation model to be modified with at least one of the one or more identified changes. Accordingly, over time, the natural language interpretation model may learn from observations of its own performance.

Abstract: Presenting data from different data providers in a correlated fashion. A first query is performed on a first data set controlled by a first entity to capture a first set of data results. Then a second query is performed on a second data set controlled by a second entity to capture a second set of data results. A relationship ontology that correlates data stored in different data stores controlled by different entities is then consulted to identify one or more relationships between data in the selected results set and the second data set.

Abstract: The modifying of a natural language interpretation model for interpreting natural language queries. The system discovers modifications that one or more queriers made to one or more original query results of one or more natural language queries to generate one or more modified query results. The system then uses the discoveries to identify one or more changes to a natural language interpretation model that would result (given the same natural language queries) in one or more query results that more accurately reflect the one or more modified query results. The system the causes the natural language interpretation model to be modified with at least one of the one or more identified changes. Accordingly, over time, the natural language interpretation model may learn from observations of its own performance.

Abstract: Presenting data from different data providers in a correlated fashion. The method includes performing a first query on a first data set controlled by a first entity to capture a first set of data results. The method further includes performing a second query on a second data set controlled by a second entity to capture a second set of data results. The method includes receiving a selection of one or more results from the first data set. The method further includes using the one or more selected results, consulting a relationship ontology that correlates data stored in different data stores controlled by different entities, to identify one or more relationships between data in the selected results set and the second data set.

Abstract: The description relates to an interactive digital display. One example includes a display device configured to receive user input and recognize commands relative to data visualizations. The system also includes a graphical user interface configured to be presented on the display device that allows users to interact with the data visualizations via the user commands.

Abstract: The techniques described herein determine configurations for data visualizations based on characteristics interpreted from input data. Input data including a plurality of images may be obtained. For instance, the input data may include one or more files containing images associated with an entity. The techniques disclosed herein may determine a characteristic, such as a primary color, based on the input data. The techniques disclosed herein may determine an individual entity or subject to be associated with the characteristic. Techniques disclosed herein also involve the generation of output data defining a visualization based on the characteristic. A rendering of the output data provides an indication of the individual entity or subject. In some configurations, a rendering of the output data provides a graphical association between data in a dataset and the individual entity or subject.

Abstract: The techniques described herein determine configurations for data visualizations based on characteristics interpreted from input data. Input data including a plurality of images may be obtained. For instance, the input data may include one or more files containing images associated with an entity. The techniques disclosed herein may determine a characteristic, such as a primary color, based on the input data. The techniques disclosed herein may determine an individual entity or subject to be associated with the characteristic. Techniques disclosed herein also involve the generation of output data defining a visualization based on the characteristic. A rendering of the output data provides an indication of the individual entity or subject. In some configurations, a rendering of the output data provides a graphical association between data in a dataset and the individual entity or subject.

Abstract: Mechanisms to help a computing system respond to a request for information within a data model. After determining that there is insufficient information within the data model to respond to the request, the computing system identifies one or more additional data sources that are external to the data model and that contain information suitable to respond to the request. The computing system then automatically supplements the data model with at least one of such additional data sources. The computing system then responds to the request using the supplemented data model. In some embodiments, the supplementation may be performed in advance of the request by analyzing the characteristics of the data model and/or by anticipating possible future requests. Thus, a data model may grow automatically in response to particular usage of that data model to satisfy requests.