Microsoft Technology Licensing, LLC

Haidong Zhang

Dongmei Zhang

Zhitao Hou

Jian Wang

Min Wang

Yantao Li

Bing Jiang Sun

Xiao Bi Liang

Wenli Zhu

He Huang

Rui Ding

Guowei Liu

Qiang Wang

Shuguang Ye

Yingnong Dang

Shi Han

Adnan Azfar Mahmud

Chunhui Zhang

Bryan Dove

Jian-Guang Lou

Zhouyu Fu

Mina Mikhail

Xiangwei Zhu

Eslam Elsawy

Sanyam Chaudhary

Nikhil Gaekwad

Bin Zhu

Xiao Yang

Bo Zhang

Daniel M Cheung

Song Ge

Yu Mao

Jim Sun

Shusen Liu

Bei Chen

Scott Ruble

Adan Azfar Mahmud

Yun Wang

Weiwei Cui

Yuanyuan Tang

Ishita Gupta

A method provides an automatic notification manner of data changes. After collecting information related to a target user 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. Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert, and a notification related to the alert may be provided to the user via various manners. 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.

Automatic notification of data changes

Implementations of the subject matter described herein relate to generating animated infographics from static infographics. A computer-implemented method comprises: extracting visual elements of a static infographic; determining, based on the visual elements, a structure of the static infographic at least indicating a layout of the visual elements in the static infographic; and applying a dynamic effect to the visual elements based on the structure of the static infographic to generate an animated infographic.

Generating animated infographics from static infographics

Various implementations of the present disclosure relate to text to visualization. In a method, information items are extracted from a natural language sentence. Visual elements associated with the information items are determined. A visual representation of the natural language sentence based on the visual elements is determined, the visual representation indicating information expressed by the natural language sentence.

Text to visualization

According to implementations of the subject matter described herein, there is provided a solution for data embedding and data extraction in images. To perform data embedding, a target region for data embedding is determined from a source image, such as a chart image. Target data to be embedded is converted into a sequence of logical values represented in a predetermined format. Based on the sequence of logical values, image values of one or more image elements in the target region are changed such that the changed image values in the target region can be used to convey the sequence of logical values corresponding to the target data. The variations in image values are within a predetermined range such that no significant data distortion of perception distortion is caused by the data embedding. In a subsequent process, the embedded data can also be easily extracted from the image for use.

Data embedding and data extraction in image

A method provides an automatic notification manner of data changes. After collecting information related to a target user () 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 (). Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert (), and a notification related to the alert may be provided to the user via various manners (). 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.

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.

Multi-dimensional data insight interaction

According to implementations of the subject matter described herein, there is proposed a solution for automatic analysis of a difference between multi-dimensional datasets. In this solution, an analysis request is received for a first dataset and a second dataset, each of which including data items corresponding to a plurality of dimensions. In response to the analysis request, data items corresponding to a first dimension in the first and second datasets are compared. Based on the comparison, a first set of influence factors associated with the first dimension are determined, each influence factor indicating a reason for a difference between the first and second datasets from a respective perspective. An analysis result related to the difference between the first and second datasets is presented based on the first set of influence factors. In this way, it is possible to achieve automatic and efficient analysis of the difference between the different datasets.

Automatic analysis of difference between multi-dimensional datasets

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.

User interface tools for visual exploration of multi-dimensional data

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.

Conversational data analysis

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.

Direct manipulation interface for data analysis

Automatically identifying insights from a dataset and presenting the insights graphically and in natural language text ranked by importance is provided. Different data types and structures in the dataset are automatic recognized and matched with a corresponding specific analysis type. The data is analyzed according to the determined corresponding analysis types, and insights from the analysis are automatically identified. The insights within a given insight type and between insight types are ranked and presented in order of importance in automatically generate charts that visually describe each insight and in natural language text that describes each insight in such a way that it may be understandable to a general audience who may not have a familiarity with statistics.

Automatic insights for spreadsheets

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Beijing, China

Haidong Zhang