Abstract: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

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: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

Abstract: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

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: Classification predictions made by a concept classifier may be interactively visualized and explored in a user interface that displays visual representations of a plurality of data items in a star coordinate space spanned by a plurality of anchor concepts each mapping the data items onto respective finite real-valued scores. Positions of the visual representations of the data items in the star coordinate space are based on the scores for the plurality of anchor concepts, and may be updated responsive to a user manipulating the anchor concepts in the user interface, e.g., by moving or modifying definitions of anchor concepts, or by adding or deleting anchor concepts. The visual representations may of the data items may reflect labels and/or classification predictions, and may be updated based on updated classification predictions following retraining the of the concept classifier based on added training data or new features.

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: A three dimensional (3D) model of an object or environment may be created from images and other information of the object or environment. The 3D model may be created by aligning the images. The 3D model may include surfaces that are based on surfaces of the object or environment. The 3D model may be displayed, exported, modified and so on.

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: Disclosed herein are technologies directed to a feature ideator. The feature ideator can initiate a classifier that analyzes a training set of data in a classification process. The feature ideator can generate one or more suggested features relating to errors generated during the classification process. The feature ideator can generate an output to cause the errors to be rendered in a format that provides for an interaction with a user. A user can review the summary of the errors or the individual errors and select one or more features to increase the accuracy of the classifier.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for visualizing a performance of a machine-learned model. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by the machine-learned model. The plurality of item representations are arranged according to scores assigned to the plurality of items by the machine-learned model. Further, each of the plurality of item representations is visually configured to represent a label assigned to a corresponding item.

Abstract: Described is a technology by which an image such as a stitched panorama is automatically cropped based upon predicted quality data with respect to filling missing pixels. The image may be completed, including by completing only those missing pixels that remain after cropping. Predicting quality data may be based on using restricted search spaces corresponding to the missing pixels. The crop is computed based upon the quality data, in which the crop is biased towards including original pixels and excluding predicted low quality pixels. Missing pixels are completed by using restricted search spaces to find replacement values for the missing pixels, and may use histogram matching for texture synthesis.

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: Aspects of the subject matter described herein relate to data visualization. In aspects, a caller sends a request to draw a set of shapes that have a shape type common among the shapes. The request may be for a static drawing or 1 of N animation frames. If the device has a graphical processing unit (GPU) that is available, GPU code is generated for the GPU to draw the set of shapes. Otherwise, CPU code is generated to draw the set of shapes. The drawing code is tailored to the constant, data-mapped, and animated attribute values specified by the caller and omits conditional statements that test values that are computable prior to drawing the shapes.

Abstract: A three dimensional (3D) model of an object or environment may be created from images and other information of the object or environment. The 3D model may be created by aligning the images. The 3D model may include surfaces that are based on surfaces of the object or environment. The 3D model may be displayed, exported, modified and so on.

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: A three dimensional (3D) model of an object or environment may be created from images and other information of the object or environment. The 3D model may be created by aligning the images. The 3D model may include surfaces that are based on surfaces of the object or environment. The 3D model may be displayed, extorted, modified and so on.