Abstract: A graphical user interface for creating a data summary table includes a pane including a plurality of fields, and a data summary table. Upon selection of a field to include in the data summary table, the user interface is programmed to analyze the field and automatically place the field in the data summary table.

Abstract: A graphical user interface for creating a data summary table includes a data summary table including a plurality of fields, and a filtering pane programmed to apply filters to items in the fields of the data summary table, the filtering pane including a field selector control, wherein the field selector control is programmed to allow for selection of a field of the fields in the data summary table to define filtering for the field.

Abstract: A graphical user interface for creating a data summary table includes a field pane including a list of a plurality of fields, and a layout pane including a plurality of zones. The zones represent areas of the data summary table, and the layout pane allows a field of the plurality of fields from the field pane to be added to a first zone of the zones. A data summary table is updated upon the field being added to the layout pane.

Abstract: A method and system associate conditional formats with selected data points or groups of data points within a summary table. In one embodiment, related groups of data are determined based upon currently highlighted cells in a summary table. A user then selects from the groups and identifies conditional formats to be associated with the specified data points. The conditional formats are associated with currently displayed as well as non-displayed data within the summary table. The associated conditions are saved relative to the data points rather than the original cell positions so that the conditions remain associated with the data points following a reorganization of the summary table.

Abstract: The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

Abstract: Methods, systems, and computer program products dynamically filter an aggregate report based on values resulting from one or more filters previously applied to the aggregate report. A method involves maintaining a collection of filters to be applied in a designated evaluation order to the aggregate report, applying a first filter to the aggregate report, and evaluating values in the aggregate report to produce aggregate values resulting from applying the first filter. The method also involves applying a next filter to the aggregate report and evaluating the aggregate values in the aggregate report resulting from applying the first filter therein producing aggregate values resulting from applying the next filter to the aggregate report. Additionally, the method involves displaying the aggregate report wherein evaluating values resulting from previously applying any filter to the aggregate report involves evaluating visual values displayed in the aggregate report after each previous filter has been applied.

Abstract: The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

Abstract: The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

Abstract: The present invention leverages curve fitting data techniques to provide automatic detection of data anomalies in a “data tube” from a data perspective, allowing, for example, detection of data anomalies such as on-screen, drill down, and drill across data anomalies in, for example, pivot tables and/or OLAP cubes. It determines if data substantially deviates from a predicted value established by a curve fitting process such as, for example, a piece-wise linear function applied to the data tube. A threshold value can also be employed by the present invention to facilitate in determining a degree of deviation necessary before a data value is considered anomalous. The threshold value can be supplied dynamically and/or statically by a system and/or a user via a user interface. Additionally, the present invention provides an indication to a user of the type and location of a detected anomaly from a top level data perspective.

Abstract: The present invention leverages machine learning techniques to provide automatic generation of conditioning variables for constructing a data perspective for a given target variable. The present invention determines and analyzes the best target variable predictors for a given target variable, employing them to facilitate the conveying of information about the target variable to a user. It automatically discretizes continuous and discrete variables utilized as target variable predictors to establish their granularity. In other instances of the present invention, a complexity and/or utility parameter can be specified to facilitate generation of the data perspective via analyzing a best target variable predictor versus the complexity of the conditioning variable(s) and/or utility. The present invention can also adjust the conditioning variables (i.e.

Abstract: The present invention relates to a method of manipulating a software application and processing data stored in a data source. The method includes receiving a natural language input and analyze the natural language input to identify semantic information contained therein. Portions of the natural language input are associated with command objects and entity objects of a schema based on the semantic information and the natural language input. The method also includes rendering data from the data source in a table of columns and rows based on the schema and the associated portions of the natural language input.