Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for context-based customization using semantic graph data. In some implementations, semantic graph data indicating objects and relationships among the objects is stored. Usage data is generated to indicate (i) levels of usage of the objects and (ii) contexts in which the objects are used. A query is received that is associated with a user and that includes data indicating a context of the user. Customized weights for connections among the objects is determined based on (i) the levels of usage indicated by the usage data and (ii) scores indicating a degree of similarity among the contexts indicated by the usage data and the context of the user. A response to the query is provided based on the customized weights for the connections among the objects indicated by the semantic graph data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for machine learning to generate and evaluate visualizations. In some implementations, a system determines properties of a dataset. The system generates visualization specifications that each define a different visualization for the dataset, wherein the visualization specifications specify different subsets of the dataset being illustrated with different visualization formats. The system evaluates the visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets. The system selects a subset of the visualization specifications based on output of the machine learning model. The system provides, for display, visualization data for the subset of visualization specifications that were selected based on the output of the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for providing action objects in a semantic graph. IN some implementations, semantic graph data indicates a semantic graph of objects and relationships between the objects. The semantic graph data defines an action object that represents an action that can be initiated, and the semantic graph data indicates a particular object type or a particular data type with which the action object can be used. An object of a particular object type or data of the particular data type is identified as being involved in processing of the one or more computers. In response to identifying the use of the object of the particular object type or data of the particular data type, an indication is provided that the action represented by the action object can be initiated.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for incorporating opinion information with semantic graph data. In some implementations, semantic graph data indicating objects relationships among the objects is stored. Opinion data indicating user sentiment with respect to the objects is generated. A particular object of the objects indicted by the semantic graph is identified. An opinion measure for the particular object is determined based on the generated opinion data. Output data is provided to a client device for presentation, where the output data is generated based on the opinion measure for the particular object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for enhanced preparation and integration of data sets. In some implementations, data indicating user input that identifies a first data set that includes streaming data and a second data set that includes non-streaming data is received. The first data set and the second data set are integrated to generate a hybrid data set. The data processing system provides access to the hybrid data set through a (i) non-streaming access channel that provides a periodically-refreshed summary of both the streaming data and the non-streaming data and (ii) a streaming access channel that provides a data stream based on combined data of the first data set and the second data set. One or more application programing interfaces are provided. The one or more application programming interfaces allow at least one client device to access the non-streaming access channel and the streaming access channel.

Abstract: Methods, systems, and apparatus, including computer programs stored on computer-readable media, for generating context data for an information card are disclosed. Upon receiving a selection of a data element to include in an information card, a plurality of dimensional view types available for the data element are determined and provided to a user. Upon receiving a selection of a dimensional view type from the plurality of dimensional view types, a set of attributes associated with the dimensional view type are determined and provided to the user. A selection of an attribute from the set of attributes is received, and a dimensional view is generated based on the selection of the dimensional view type and attribute.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for providing information cards using semantic graph data. In some implementations, semantic graph data for a semantic graph is stored, where the semantic graph data indicates objects and relationships among the objects, and the objects include a card object that represents characteristics of an information card. A request is received from a client device, and the request is processed using the semantic graph data. Data for the information card is provided to the client device based on the card object indicated by the semantic graph data.

Abstract: Methods, systems, and apparatus, including computer programs stored on computer-readable media, for generating context data for an information card are disclosed. Upon receiving a selection of a data element to include in an information card, a plurality of dimensional view types available for the data element are determined and provided to a user. Upon receiving a selection of a dimensional view type from the plurality of dimensional view types, a set of attributes associated with the dimensional view type are determined and provided to the user. A selection of an attribute from the set of attributes is received, and a dimensional view is generated based on the selection of the dimensional view type and attribute.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for machine learning to generate and evaluate visualizations. In some implementations, a system determines properties of a dataset. The system generates visualization specifications that each define a different visualization for the dataset, wherein the visualization specifications specify different subsets of the dataset being illustrated with different visualization formats. The system evaluates the visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets. The system selects a subset of the visualization specifications based on output of the machine learning model. The system provides, for display, visualization data for the subset of visualization specifications that were selected based on the output of the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for machine learning to generate and evaluate visualizations. In some implementations, a system determines properties of a dataset. The system generates visualization specifications that each define a different visualization for the dataset, wherein the visualization specifications specify different subsets of the dataset being illustrated with different visualization formats. The system evaluates the visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets. The system selects a subset of the visualization specifications based on output of the machine learning model. The system provides, for display, visualization data for the subset of visualization specifications that were selected based on the output of the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs stored on computer-readable media, for generating context data for an information card are disclosed. Upon receiving a selection of a data element to include in an information card, a plurality of dimensional view types available for the data element are determined and provided to a user. Upon receiving a selection of a dimensional view type from the plurality of dimensional view types, a set of attributes associated with the dimensional view type are determined and provided to the user. A selection of an attribute from the set of attributes is received, and a dimensional view is generated based on the selection of the dimensional view type and attribute.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for enhanced preparation and integration of data sets. In some implementations, data indicating user input that identifies a first data set that includes streaming data and a second data set that includes non-streaming data is received. The first data set and the second data set are integrated to generate a hybrid data set. The data processing system provides access to the hybrid data set through a (i) non-streaming access channel that provides a periodically-refreshed summary of both the streaming data and the non-streaming data and (ii) a streaming access channel that provides a data stream based on combined data of the first data set and the second data set. One or more application programing interfaces are provided. The one or more application programming interfaces allow at least one client device to access the non-streaming access channel and the streaming access channel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for indicating synonym relationships using semantic graph data. In some implementations, semantic graph data indicates a semantic graph that includes elements representing objects and that indicates relationships among the objects. Synonym data that indicates synonym relationships among terms associated with the objects represented by the elements is stored in association with the semantic graph data. A request to provide information using the semantic graph is received. One or more results are generated based at least in part on the synonym data stored in association with the elements of the semantic graph. The one or more results are provided in a response to the request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for incorporating opinion information with semantic graph data. In some implementations, semantic graph data indicating objects relationships among the objects is stored. Opinion data indicating user sentiment with respect to the objects is generated. A particular object of the objects indicted by the semantic graph is identified. An opinion measure for the particular object is determined based on the generated opinion data. Output data is provided to a client device for presentation, where the output data is generated based on the opinion measure for the particular object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for providing action objects in a semantic graph. IN some implementations, semantic graph data indicates a semantic graph of objects and relationships between the objects. The semantic graph data defines an action object that represents an action that can be initiated, and the semantic graph data indicates a particular object type or a particular data type with which the action object can be used. An object of a particular object type or data of the particular data type is identified as being involved in processing of the one or more computers. In response to identifying the use of the object of the particular object type or data of the particular data type, an indication is provided that the action represented by the action object can be initiated.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for machine learning to generate and evaluate visualizations. In some implementations, a system determines properties of a dataset. The system generates visualization specifications that each define a different visualization for the dataset, wherein the visualization specifications specify different subsets of the dataset being illustrated with different visualization formats. The system evaluates the visualization specifications using a machine learning model trained based on user feedback for visualizations for multiple datasets. The system selects a subset of the visualization specifications based on output of the machine learning model. The system provides, for display, visualization data for the subset of visualization specifications that were selected based on the output of the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for improving system performance using semantic graph data. In some implementations, semantic graph data indicating objects and relationships among the objects is stored. Performance measures for computing operations that access the objects are determined. The performance measures are stored in association with elements of the semantic graph data corresponding to the respective objects accessed. A subset of the performance measures are aggregated based on the semantic graph data. A configuration of one or more computing devices is altered based on the aggregated subset of the performance measures.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for context-based customization using semantic graph data. In some implementations, semantic graph data indicating objects and relationships among the objects is stored. Usage data is generated to indicate (i) levels of usage of the objects and (ii) contexts in which the objects are used. A query is received that is associated with a user and that includes data indicating a context of the user. Customized weights for connections among the objects is determined based on (i) the levels of usage indicated by the usage data and (ii) scores indicating a degree of similarity among the contexts indicated by the usage data and the context of the user. A response to the query is provided based on the customized weights for the connections among the objects indicated by the semantic graph data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for providing information cards using semantic graph data. In some implementations, semantic graph data for a semantic graph is stored, where the semantic graph data indicates objects and relationships among the objects, and the objects include a card object that represents characteristics of an information card. A request is received from a client device, and the request is processed using the semantic graph data. Data for the information card is provided to the client device based on the card object indicated by the semantic graph data.