Abstract: A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Abstract: A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Abstract: A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Abstract: A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Abstract: A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Abstract: A data visualization application transforms visualized data through visual analytics. A detected gesture and associated visualization are processed through a visual analytics engine of the application. The visual analytics engine determines attributes for a new visualization based on the contextual information of the gesture and the visualization. The analytics engine dynamically builds an action based on the attributes. Execution of the action generates the new visualization which is rendered for presentation.

Abstract: A smart fill feature is provided, which may be utilized to provide suggestions to a user and to prepopulate fill results in a spreadsheet. Characteristics associated with a set of data may be recognized and used to help determine fill results Suggestions may be determined from internal and or/external data sources, and may use contextual information in a data set to determine what a user may be trying to accomplish. External data sources may include one or more Internet data sources or internal corporate databases, providing a large amount of data from which to search for potential fill results. A split column command may be used to split a column into two or more columns. Data may be split between columns or combined by various methods, which may include, by example, by direct manipulation via tokens, or via a slider UI.

Abstract: A data visualization application recommends context based actions for data visualizations. The application displays a visualization of a dataset. In response to detecting a query request associated with the visualization, contextual information for the data is used to construct a query. The application submits the query to search services for search indexes, structured data, and unstructured data. The returned results are displayed in summary format using actionable items. The actionable items update the visualization or provide new visualizations representing the results.

Abstract: A data visualization application provides an automated presentation of visualized data. A visualization of data is generated based on contextual information. Alternate visualizations displayed as actionable suggestions are also generated based on the contextual information. The application displays visualization and the actionable suggestions in proximity. The visualization is updated with an alternate visualization associated with a selected actionable suggestion in response to a user action selecting the actionable suggestion.

Abstract: A data visualization application transforms visualized data through visual analytics. A detected gesture and associated visualization are processed through a visual analytics engine of the application. The visual analytics engine determines attributes for a new visualization based on the contextual information of the gesture and the visualization. The analytics engine dynamically builds an action based on the attributes. Execution of the action generates the new visualization which is rendered for presentation.

Abstract: A data visualization application automatically manipulates visualized data based on interactivity. Detected gestures such as touch actions, visual and audio commands, and eye-tracking are matched to an associated operation to be applied to data of the visualization. The operations include expansion, reduction, merge, split, zoom in, zoom out, style change, and similar ones. The operation is executed on the data of the visualization resulting in changes to the data. The visualization is updated to display the changes to the data.