Abstract: A stroller includes a support frame mounted on pneumatic tires that allow the stroller to easily traverse irregular or soft terrain. The support frame includes a handlebar assembly having a hammock-style seat mounted thereon. A canopy suspended above the seat contains integral speakers for streaming music from an electronic device and light strings for illuminating a seated child. The seat also includes a heater and a cooling fan for comforting a seated child during extreme temperatures.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.

Abstract: Methods, systems, and computer programs are presented for obtaining histogram data from a database utilizing an interface with histogram-related options. One method includes an operation for providing, by a server, an application programming interface (API), to access the database, which includes a histogram request, to obtain histogram data from the database, with histogram options. The server receives, from a client device, a first histogram request including histogram options. Additionally, the method includes an operation for identifying bins for the histogram based on the one or more histogram options. For each bin, the server accesses the database to obtain data for each bin. The server returns, to the client device, the histogram data for the histogram as a table containing bin values for all the bins, where the client device is configured to present the histogram to a user based on the histogram data.

Abstract: Techniques for presenting data visualizations are described. Consistent with some embodiments of the present invention, a data visualization system includes an approximate query engine to derive approximate query results and present approximate data visualizations, in interactive time. The data visualization system also includes a precise query engine, thereby enabling a data analyst to selectively request that a precise query result be generated for a specific query, so that the data analyst can confirm a data observation made when viewing the approximate visualization for that specific query. The precise visualization is presented in a manner that allows the data analyst to view, and thus confirm or disprove, a data observation recorded when viewing the approximate visualization, and also compare the precise visualization with the previously generated and presented approximate visualization.

Abstract: A visualization system suitable for visualizing differences between data sets is provided. The system determines a first data set and a second data set along with a difference between the first and second data set. A value corresponding to each unit of display of the second data set is plotted on at least one graph. The visualization system determines, for each unit of display of the second data set, whether the first data set has a value for the unit of display. In response to the first data set having the value, the visualization system plots a difference of the value of the first data set on the at least one graph. In response to the first data set not having the value, the visualization system plots a missing structure element on the at least one graph. The at least one graph is displayed to a user.

Abstract: Functionality is described herein for directly interacting with parts of a data visualization. For instance, the functionality allows a user to directly interact with data items to filter them out from the data visualization, and later restore them to the data visualization. The functionality also allows a user to directly interact with an axis to sort the data items in the data visualization. The functionality also allows a user to directly interact with a label of the data visualization to choose a new label, and to reorganize the information represented by the data visualization in response thereto. Further, before finalizing any update to the data visualization, the functionality may provide a preview of the updated data visualization.

Abstract: Functionality is described herein for directly interacting with parts of a data visualization. For instance, the functionality allows a user to directly interact with data items to filter them out from the data visualization, and later restore them to the data visualization. The functionality also allows a user to directly interact with an axis to sort the data items in the data visualization. The functionality also allows a user to directly interact with a label of the data visualization to choose a new label, and to reorganize the information represented by the data visualization in response thereto. Further, before finalizing any update to the data visualization, the functionality may provide a preview of the updated data visualization.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.

Abstract: Functionality is described herein for directly interacting with parts of a data visualization. For instance, the functionality allows a user to directly interact with data items to filter them out from the data visualization, and later restore them to the data visualization. The functionality also allows a user to directly interact with an axis to sort the data items in the data visualization. The functionality also allows a user to directly interact with a label of the data visualization to choose a new label, and to reorganize the information represented by the data visualization in response thereto. Further, before finalizing any update to the data visualization, the functionality may provide a preview of the updated data visualization.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.

Abstract: Methods, systems, and computer programs are presented for obtaining histogram data from a database utilizing an interface with histogram-related options. One method includes an operation for providing, by a server, an application programming interface (API), to access the database, which includes a histogram request, to obtain histogram data from the database, with histogram options. The server receives, from a client device, a first histogram request including histogram options. Additionally, the method includes an operation for identifying bins for the histogram based on the one or more histogram options. For each bin, the server accesses the database to obtain data for each bin. The server returns, to the client device, the histogram data for the histogram as a table containing bin values for all the bins, where the client device is configured to present the histogram to a user based on the histogram data.

Abstract: Techniques for presenting data visualizations are described. Consistent with some embodiments of the present invention, a data visualization system includes an approximate query engine to derive approximate query results and present approximate data visualizations, in interactive time. The data visualization system also includes a precise query engine, thereby enabling a data analyst to selectively request that a precise query result be generated for a specific query, so that the data analyst can confirm a data observation made when viewing the approximate visualization for that specific query. The precise visualization is presented in a manner that allows the data analyst to view, and thus confirm or disprove, a data observation recorded when viewing the approximate visualization, and also compare the precise visualization with the previously generated and presented approximate visualization.

Abstract: A visualization system suitable for visualizing differences between data sets is provided. The system determines a first data set and a second data set along with a difference between the first and second data set. A value corresponding to each unit of display of the second data set is plotted on at least one graph. The visualization system determines, for each unit of display of the second data set, whether the first data set has a value for the unit of display. In response to the first data set having the value, the visualization system plots a difference of the value of the first data set on the at least one graph. In response to the first data set not having the value, the visualization system plots a missing structure element on the at least one graph. The at least one graph is displayed to a user.

Abstract: Functionality is described herein for directly interacting with parts of a data visualization. For instance, the functionality allows a user to directly interact with data items to filter them out from the data visualization, and later restore them to the data visualization. The functionality also allows a user to directly interact with an axis to sort the data items in the data visualization. The functionality also allows a user to directly interact with a label of the data visualization to choose a new label, and to reorganize the information represented by the data visualization in response thereto. Further, before finalizing any update to the data visualization, the functionality may provide a preview of the updated data visualization.

Abstract: Functionality is described herein for directly interacting with parts of a data visualization. For instance, the functionality allows a user to directly interact with data items to filter them out from the data visualization, and later restore them to the data visualization. The functionality also allows a user to directly interact with an axis to sort the data items in the data visualization. The functionality also allows a user to directly interact with a label of the data visualization to choose a new label, and to reorganize the information represented by the data visualization in response thereto. Further, before finalizing any update to the data visualization, the functionality may provide a preview of the updated data visualization.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a technique that can include obtaining a relational query that references one or more data items and associating progress intervals with the data items. The technique can also include converting the relational query into a corresponding streaming query, and providing the streaming query and the data items with the progress intervals to a stream engine that produces incremental results of the query. For example, the progress intervals can be based on row numbers of a relational database table. The progress intervals can be used to define event lifetimes of streaming events that are provided as inputs to the stream engine.

Abstract: Multivariate data that includes data records may be obtained, each of the data records represented as data values of data attribute variables. A cardinality of the data records is substantially large. A display layout template representing a collectivized visualization of dataset points is obtained, the collectivized visualization logically associated with a set of the data attribute variables, from a perspective of a user viewer. An individualized particulate display representation for each of the data records is determined, based on a portion of the data values associated with each respective data record. The individualized particulate display representation includes a discrete, individually selectable particulate shape and a display location relative to geometric bounds logically associated with the template. Display of the individualized particulate display representations is initiated in accordance with the template.

Abstract: A system described herein includes a receiver component that receives a tree-structured dataset that includes multiple branches that are hierarchically related to one another. The system also includes an executor component that causes a programmable graphical processing unit to generate a Voronoi treemap based at least in part upon the tree-structured dataset, wherein the Voronoi treemap comprises a plurality of subareas that correspond to the multiple branches, and wherein the Voronoi treemap represents hierarchical relationships between the multiple branches.

Abstract: Incremental query results and confidence interval values associated with respective incremental query results may be obtained. Visualization shape objects indicating uncertainty values may be determined, based on mapping values of respective incremental query results and confidence interval values to points in the associated visualization shape objects, the uncertainty values visualized based on proportional shapes of the visualization shape objects. At least one visualization comparison object representing a comparison of a plurality of distributions associated with the obtained incremental query results and confidence interval values may be determined. Display of the plurality of visualization shape objects and the at least one visualization comparison object may be initiated.