Abstract: According to implementations of the present disclosure, there is proposed a solution for analyzing a data table in response to a user input. In this solution, a user input in a cell of a data table is determined. The data table comprises a plurality of cells arranged in rows and columns. An analysis operation for the data table is determined based on semantics of the data table and the user input, the analysis operation corresponding to the user input. Further, a result of the analysis operation is presented in a region of the data table related to the cell. In this way, grid characteristics of the data table can be utilized to provide the result of the analysis operation as desired by a user and simple, efficient and user-friendly data analysis can be facilitated.

Abstract: A computing apparatus of an insight interfacing system receives from a user a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure. Insights are received based on the received request then presented on a display device. Also, a chart may be presented based on the received request. The computing apparatus receives a selection of at least a portion of the presented one or more insights or a portion of the chart, then receives contextual insights based on the selected portion and presents the contextual insights on the display device.

Abstract: A computing apparatus of an insight interfacing system receives from a user a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure. Insights are received based on the received request then presented on a display device. Also, a chart may be presented based on the received request. The computing apparatus receives a selection of at least a portion of the presented one or more insights or a portion of the chart, then receives contextual insights based on the selected portion and presents the contextual insights on the display device.

Abstract: A computing device running a tool for visually exploring datasets receives a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure and in response to receiving a user selection of a data point associated with the dataset, presents a visualization of the calculations associated with the selected data point. The tool allows the user to select one of the calculations, thus causing presentation of a visualization of the selected calculation. A semantic graphical feature is incorporated into the visualization of the selected calculation depending upon a type of the calculation.

Abstract: Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.

Abstract: A computing device running a tool for visually exploring datasets receives a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure and in response to receiving a user selection of a data point associated with the dataset, presents a visualization of the calculations associated with the selected data point. The tool allows the user to select one of the calculations, thus causing presentation of a visualization of the selected calculation. A semantic graphical feature is incorporated into the visualization of the selected calculation depending upon a type of the calculation.

Abstract: A computing device running a tool for visually exploring datasets receives a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure and in response to receiving a user selection of a data point associated with the dataset, presents a visualization of the calculations associated with the selected data point. The tool allows the user to select one of the calculations, thus causing presentation of a visualization of the selected calculation. A semantic graphical feature is incorporated into the visualization of the selected calculation depending upon a type of the calculation.

Abstract: Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.

Abstract: A method provides an automatic notification manner of data changes. After collecting information related to a target user (202) such as a dataset, a data dashboard, or a data report, the analysis preference of the user can be determined based on the collected information (204). Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert (206), and a notification related to the alert may be provided to the user via various manners (208). The method does not require the user to manually configure or create an alert rule for data changes, which makes data-driven alerting much easier for the user, thereby improving the user experience.

Abstract: A computing device running a tool for visually exploring datasets receives a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure and in response to receiving a user selection of a data point associated with the dataset, presents a visualization of the calculations associated with the selected data point. The tool allows the user to select one of the calculations, thus causing presentation of a visualization of the selected calculation. A semantic graphical feature is incorporated into the visualization of the selected calculation depending upon a type of the calculation.

Abstract: A computing device running a tool for visually exploring datasets receives a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure and in response to receiving a user selection of a data point associated with the dataset, presents a visualization of the calculations associated with the selected data point. The tool allows the user to select one of the calculations, thus causing presentation of a visualization of the selected calculation. A semantic graphical feature is incorporated into the visualization of the selected calculation depending upon a type of the calculation.

Abstract: Implementations are described related to users directly manipulating visual depictions of aspects of a data set displayed in a user interface to generate or modify visual representations of the data set, such as a chart. The direct manipulation may be either through a computer input device, (e.g., a keyboard or mouse), or based on other input data, such as a touch input. Aspects of a data set include dimensions, which may be used to filter the data set, and metrics, which are quantifiable elements within the data set. In various implementations, input such as drag-and-drop operations corresponding to visual depictions of aspects of a data set may be interpreted in generating or modifying charts.

Abstract: A computing device running a tool for visually exploring datasets receives a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure and in response to receiving a user selection of a data point associated with the dataset, presents a visualization of the calculations associated with the selected data point. The tool allows the user to select one of the calculations, thus causing presentation of a visualization of the selected calculation. A semantic graphical feature is incorporated into the visualization of the selected calculation depending upon a type of the calculation.

Abstract: A computing apparatus of an insight interfacing system receives from a user a request for a dataset comprising a plurality of subspaces of a multi-dimensional data structure. Insights are received based on the received request then presented on a display device. Also, a chart may be presented based on the received request. The computing apparatus receives a selection of at least a portion of the presented one or more insights or a portion of the chart, then receives contextual insights based on the selected portion and presents the contextual insights on the display device.

Abstract: Implementations are described related to users directly manipulating visual depictions of aspects of a data set displayed in a user interface to generate or modify visual representations of the data set, such as a chart. The direct manipulation may be either through a computer input device, (e.g., a keyboard or mouse), or based on other input data, such as a touch input. Aspects of a data set include dimensions, which may be used to filter the data set, and metrics, which are quantifiable elements within the data set. In various implementations, input such as drag-and-drop operations corresponding to visual depictions of aspects of a data set may be interpreted in generating or modifying charts.

Abstract: Implementations are described related to users directly manipulating visual depictions of aspects of a data set displayed in a user interface to generate or modify visual representations of the data set, such as a chart. The direct manipulation may be either through a computer input device, (e.g., a keyboard or mouse), or based on other input data, such as a touch input. Aspects of a data set include dimensions, which may be used to filter the data set, and metrics, which are quantifiable elements within the data set. In various implementations, input such as drag-and-drop operations corresponding to visual depictions of aspects of a data set may be interpreted in generating or modifying charts.

Abstract: Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.

Abstract: Data visualizations may include a large number of data points, some of which may be small, and/or data points that are in close proximity to one another. To assist a user in accurately selecting a desired data point, when displaying a data visualization, a magnification area is simultaneously displayed. The magnification area includes a zoomed-in view of a portion of the data visualization, centered at a current location indicated by a selection device. The magnification area also includes centered vertical and horizontal crosshairs to visually indicate the current location indicated by the selection device.

Abstract: A data exploration user interface includes a selection area with selectable representations of queryable fields of a data source and a visualization area where query results are displayed as data visualizations. Queries are generated by dragging fields from the selectable area to the visualization area of the user interface. A tree structure of data visualizations may be created by dragging data points out of a displayed visualization and applying additional fields to create a new query and resulting visualization. The tree structure is graphically represented with path indicators that provide historical context for each new data visualization within the visualization are of the user interface.

Abstract: Techniques and arrangements for creating and editing motion data stories are described herein. In some implementations, the techniques and arrangements may determine semantic differences between consecutive slides intended to be used as the basis for a motion data story, and use the determined differences to determine appropriate transitional animations and/or animation effects. In addition to determined semantic differences, templates may also be used to determine the transitional animations and/or animation effects.