Abstract: 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.

Abstract: 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.

Abstract: The subject matter described herein relates to table detection in a spreadsheet. According to implementations of the subject matter described herein, there is proposed a solution for determining a table in a spreadsheet. In the solution, respective multiple attributes of multiple cells comprised in the spreadsheet may be extracted. Respective features of the multiple cells may be determined based on the extracted multiple attributes. The multiple cells may be divided into at least one candidate area based on the features. At least one candidate table in the spreadsheet may be determined based on the at least one candidate area. By means of the solution, respective features of the multiple cells comprised in the spreadsheet may be determined based on the respective attributes of the multiple cells, and further, a candidate region where a table might exist may be determined based on the respective features of the multiple cells.

Abstract: The subject matter described herein relates to table detection in a spreadsheet. According to implementations of the subject matter described herein, there is proposed a solution for determining a table in a spreadsheet. In the solution, respective multiple attributes of multiple cells comprised in the spreadsheet may be extracted. Respective features of the multiple cells may be determined based on the extracted multiple attributes. The multiple cells may be divided into at least one candidate area based on the features. At least one candidate table in the spreadsheet may be determined based on the at least one candidate area. By means of the solution, respective features of the multiple cells comprised in the spreadsheet may be determined based on the respective attributes of the multiple cells, and further, a candidate region where a table might exist may be determined based on the respective features of the multiple cells.

Abstract: 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.

Abstract: The subject matter described herein relates to table detection in a spreadsheet. According to implementations of the subject matter described herein, there is proposed a solution for determining a table in a spreadsheet. In the solution, respective multiple attributes of multiple cells comprised in the spreadsheet may be extracted. Respective features of the multiple cells may be determined based on the extracted multiple attributes. The multiple cells may be divided into at least one candidate area based on the features. At least one candidate table in the spreadsheet may be determined based on the at least one candidate area. By means of the solution, respective features of the multiple cells comprised in the spreadsheet may be determined based on the respective attributes of the multiple cells, and further, a candidate region where a table might exist may be determined based on the respective features of the multiple cells.

Abstract: 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.

Abstract: According to implementations of the present disclosure, there is provided a solution for auto-formatting of a data table. A computer-implemented method comprises obtaining values of at least one attribute for a plurality of cells in a data table, the values of the at least one attribute indicating at least one of a semantic meaning of data filled in the cells or a structure of the data table, the cells being arranged in rows and columns in the data table; extracting a feature representation of the values of the at least one attribute In using a first learning network; and determining respective formats for the cells based on the feature representation using at least the first learning network. In this way, it is possible to implement automatic mapping from attribute settings of cells to cell formats by using a learning network, thereby achieving auto-formatting of the data table.

Abstract: The subject matter described herein relates to table detection in a spreadsheet. According to implementations of the subject matter described herein, there is proposed a solution for determining a table in a spreadsheet. In the solution, respective multiple attributes of multiple cells comprised in the spreadsheet may be extracted. Respective features of the multiple cells may be determined based on the extracted multiple attributes. The multiple cells may be divided into at least one candidate area based on the features. At least one candidate table in the spreadsheet may be determined based on the at least one candidate area. By means of the solution, respective features of the multiple cells comprised in the spreadsheet may be determined based on the respective attributes of the multiple cells, and further, a candidate region where a table might exist may be determined based on the respective features of the multiple cells.

Abstract: 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.