Microsoft Technology Licensing, LLC

Beijing X-charge Co., Ltd.

Rui Ding

Dongmei Zhang

Shi Han

Haidong Zhang

Zhitao Hou

Yingnong Dang

Zhouyu Fu

Mina Mikhail

Nikhil Gaekwad

Eslam Elsawy

Sanyam Chaudhary

Qiang Wang

Fei Liu

Qiang Fu

Qianchuan Zhao

Haoyu Jiang

Shulei Wang

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

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

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 form the analysis are automatically identified. The insights within a given insight type and between insight types are ranked and presented in order of importance. Insights include those having multiple pipelined attributes and other insights include multiple insights identified as having some relationship for the included insights.

Automatic insights for multi-dimensional data

Invention enclosed herein discloses a time delay toll system for charging piles, which can promote an electric automobile user to leave as early as possible and/or settle accounts as quick as possible after the charging is completed, improve the availability factor of the charging piles and parking stalls, and thereby increasing an efficiency of operation of the charging piles. The time delay toll system comprising: a charging state inspection module, which is configured to detect a magnitude of each communication cable of a plurality of communication cables installed in the charging piles, and send the magnitude of the voltage to a microprocessor; a parking detection module, which is installed on a parking stall, and is configured to detect whether there is an automobile on said parking stall; a microprocessor, which is configured to determine whether the charging of said automobile is competed according to a magnitude of a voltage of a communication cable associated with said automobile, if said charging is completed, notify a timer to reckon by time to generate time elapse data associated with said charging, which is a time period from said automobile driving in the parking stall for charging to leaving the parking stall when the charging is completed; and a fee deduction module, which is configured to calculate a charge expense and a time delay expense, and to deduct the said charge expense and said time delay expense from a user fund previously established for an owner of said electric automobile. A method that adopts this time delay toll system for charging piles is also disclosed.

Time delay toll system for charging piles and its method

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

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. Insights include those having multiple pipelined attributes and other insights include multiple insights identified as having some relationship for the included insights.

The techniques described herein provide tools that summarize a dataset by creating a final set of segments that, when visually presented via a histogram or other data presentation tool, show the distribution of at least a portion of the data. To create the final set of segments, the techniques described herein may collect or receive a dataset with distinct values, and divide the dataset into a number of segments that is less than or equal to a segment presentation threshold (e.g., ten segments). After creating the final set of segments, the techniques may configure and/or present data visualizations, such as histograms, for the created segments so that an observer is provided with a good viewing experience.

Data segmentation and visualization

In some examples, a time-series data set can be analyzed and grouped in a fast and efficient manner. For instance, fast grouping of multiple time-series into clusters can be implemented through data reduction, determining cluster population, and fast matching by locality sensitive hashing. In some situations, a user can select a level of granularity for grouping time-series into clusters, which can involve trade-offs between the number of clusters and the maximum distance between two time-series in a cluster.

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Beijing, China

Rui Ding