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: Methods for efficient display of data points in a user interface are performed by systems and apparatuses. Efficient display of data point in a user interface includes maximizing coverage of data points prior to rendering. Coverage is determined using a radius value for represented data points in a data set. The radius may be increased to correspondingly generate additional coverage. Covered data points may be removed from the rendering subset as the radius is set and increased. The radius is increased until the number of represented data points to render is less than a threshold value. Multiple data sets may be efficiently rendered together.

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: Generating a user interface visualizing a table data structure. A table data structure comprising a plurality of rows and a plurality of columns is accessed. A column to use for sorting each row into a different region of the user interface is identified. Region characteristics, usable for matching which row(s) is to be visualized within a corresponding region, are identified. Each region is associated with a different subset of row(s) that are to be visualized within the region. Each subset comprises rows having a value that matches the region characteristics of the associated region at an intersection between the column and the row in the table data structure. A visual representation of each row is displayed within the user interface. The display includes a visual arrangement of each visual representation into a corresponding region. Each visual representation is arranged within the region the row associated with the visual representation belongs.

Abstract: Methods for efficient display of data points in a user interface are performed by systems and apparatuses. Efficient display of data point in a user interface includes maximizing coverage of data points prior to rendering. Coverage is determined using a radius value for represented data points in a data set. The radius may be increased to correspondingly generate additional coverage. Covered data points may be removed from the rendering subset as the radius is set and increased. The radius is increased until the number of represented data points to render is less than a threshold value. Multiple data sets may be efficiently rendered together.

Abstract: Methods for efficient display of data points in a user interface are performed by systems and apparatuses. Efficient display of data point in a user interface includes maximizing coverage of data points prior to rendering. Coverage is determined using a radius value for represented data points in a data set. The radius may be increased to correspondingly generate additional coverage. Covered data points may be removed from the rendering subset as the radius is set and increased. The radius is increased until the number of represented data points to render is less than a threshold value. Multiple data sets may be efficiently rendered together.

Abstract: Methods for efficient display of data points in a user interface are performed by systems and apparatuses. Efficient display of data point in a user interface includes maximizing coverage of data points prior to rendering. Coverage is determined using a radius value for represented data points in a data set. The radius may be increased to correspondingly generate additional coverage. Covered data points may be removed from the rendering subset as the radius is set and increased. The radius is increased until the number of represented data points to render is less than a threshold value. Multiple data sets may be efficiently rendered together.

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: Generating a user interface visualizing a table data structure. A table data structure comprising a plurality of rows and a plurality of columns is accessed. A column to use for sorting each row into a different region of the user interface is identified. Region characteristics, usable for matching which row(s) is to be visualized within a corresponding region, are identified. Each region is associated with a different subset of row(s) that are to be visualized within the region. Each subset comprises rows having a value that matches the region characteristics of the associated region at an intersection between the column and the row in the table data structure. A visual representation of each row is displayed within the user interface. The display includes a visual arrangement of each visual representation into a corresponding region. Each visual representation is arranged within the region the row associated with the visual representation belongs.

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: The displaying of data from a table in a manner that at least some of the rows are each represented with row visualization, and in which the positioning of the row visualization conveys information regarding column values of the row. In particular, those row visualizations that correspond to rows in which one or more column values satisfy certain value characteristics are clustered within a common region. Thus, intuitive knowledge of the characteristics of the rows may be obtained by simply noticing the position of the row visualization within the user interface. In some cases, a given row visualization may have a control that may be interacted with to show a more detailed row representation, or a given region may have a control that may be interacted with to show further details regarding the rows represented within that region.

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: 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: Displaying a visualization of ranked elements over a selected dimension. The method includes determining a user selection of a ranking function for a plurality of elements. The ranking function defines a core value to be ranked. The method further includes determining a dimension over which the core value of the ranking function output can change. The method further includes animating a relevant number of the elements over time. A time value of the animation correspond to values of the dimension. Animating includes displaying elements in prominence corresponding to the result of the output of the ranking function.

Abstract: The displaying of data from a table in a manner that at least some of the rows are each represented with row visualization, and in which the positioning of the row visualization conveys information regarding column values of the row. In particular, those row visualizations that correspond to rows in which one or more column values satisfy certain value characteristics are clustered within a common region. Thus, intuitive knowledge of the characteristics of the rows may be obtained by simply noticing the position of the row visualization within the user interface. In some cases, a given row visualization may have a control that may be interacted with to show a more detailed row representation, or a given region may have a control that may be interacted with to show further details regarding the rows represented within that region.