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: Smart defaults are provided for data visualization by creating a default layout of rows, columns, filters, and comparable elements that improve a user's experience in finding relevant answers within the data. Usage history of the ways that users look at data in various data sources, user specific information, and inferred relationships between a current user and similar users are used to determine elements relevant to visualization of data for a particular user such that the visualization process may be automatically started, and a relevance model is formed/adjusted based on these factors. Queries may also be executed in a preemptive fashion based on the relevance model and results provided to a requesting user more rapidly enhancing user experience with networked data visualization.

Abstract: A mechanism for responding to a natural language query. Linguistic parsing is applied to the natural language query to generate a set of language element(s). One or more of those language elements are mapped to parameters and/or data elements of a parameterized report, which is selected for use in responding to the query based on the mapping. The selection of the parameterized report and/or mapping to the parameterized report may use the schema of the underlying dataset that the parameterized report uses in order to perform more precise mapping and selection. The selected parameterized report is then used to construct structured data that is responsive to the query. For instance, the structured data could include visualizations. In this manner, already existing parameterized reports that were previously used to generated structured data in the form of reports, may again be used in order to respond to natural language queries.

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: A mechanism for responding to a natural language query. Linguistic parsing is applied to the natural language query to generate a set of language element(s). One or more of those language elements are mapped to parameters and/or data elements of a parameterized report, which is selected for use in responding to the query based on the mapping. The selection of the parameterized report and/or mapping to the parameterized report may use the schema of the underlying dataset that the parameterized report uses in order to perform more precise mapping and selection. The selected parameterized report is then used to construct structured data that is responsive to the query. For instance, the structured data could include visualizations. In this manner, already existing parameterized reports that were previously used to generated structured data in the form of reports, may again be used in order to respond to natural language queries.

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 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.

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: Search suggestions are generated in manner that takes into account access-control information. A query can be received from a user of a search engine prior to initiating execution of the query. Data that is accessible to the user can be determined based on access information associated with the user and data. Subsequently, query suggestions can be generated dynamically based on data accessible to the user.

Abstract: A mechanism to visualize data to a user in a sufficient manner. The user selects a visualization type to visualize a selected subset of a data model. To fit the data well into a visualization of that visualization type, the system then evaluates the user selections of the visualization type of the subset of data against the rule set. Based on the evaluation, the system determines that the subset of data overpopulates the visualization type. In some embodiments, the system further identifies one or more filters to apply to the subset of data which would decrease the population of data within the virtualization type. Then, a visualization of the selected visualization type is to be displayed using at least one of the one or more identified filter.

Abstract: A mechanism to visualize data to a user in a sufficient manner. The user selects a visualization type to visualize a selected subset of a data model. To fit the data well into a visualization of that visualization type, the system then evaluates the user selections of the visualization type of the subset of data against the rule set. Based on the evaluation, the system determines that the subset of data does not populate or insufficiently populates the visualization type. In some embodiments, the system further recommends additional data to supplement the selected subset of data to more sufficiently utilize the visualization to display the subset of data in conjunction with the supplemented data. The system may further display the visualization based on the selected subset of the data model perhaps before and/or after supplemented with the supplemented data.

Abstract: Providing visualizations based on natural language searches. A method includes receiving a natural language query from a client. The method further includes based on the natural language query, obtaining a semantic model of the natural language query. The method further includes based on the semantic model, obtaining a list of a plurality of visualizations, the visualizations being based on a bias ranking of the visualizations in the list. The method further includes providing the list of the plurality of visualizations to the client, where at the client a set of visualization construction rules are applied to select a visualization from the list to apply results from the natural language query to the visualization.

Abstract: Smart defaults are provided for data visualization by creating a default layout of rows, columns, filters, and comparable elements that improve a user's experience in finding relevant answers within the data. Usage history of the ways that users look at data in various data sources, user specific information, and inferred relationships between a current user and similar users are used to determine elements relevant to visualization of data for a particular user such that the visualization process may be automatically started, and a relevance model is formed/adjusted based on these factors. Queries may also be executed in a preemptive fashion based on the relevance model and results provided to a requesting user more rapidly enhancing user experience with networked data visualization.

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.