Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

Abstract: At least one analytical operation from a set of different analytical operations may be determined based on at least one input. The input(s) may comprise contextual information of working content being displayed to a user on a device and comprising numerical data. Supplemental information for the working content may be generated using the determined analytical operation(s), may comprise a numerical-based analysis of the numerical data, and may be caused to be displayed to the user concurrently with the working content. The contextual information may comprise structured data. The input(s) may further comprise at least one of a history of the user's interactions with the working content, a history of the user's interactions with recommendations of supplemental information for the working content, a history of other users' interactions with the working content, and a history of other users' interactions with recommendations of supplemental information for the working content.

Abstract: Techniques of providing visualization suggestions are disclosed. In some example embodiments, a current visualization of at least a portion of data of a dataset is determined to be displayed to a user in a graphical user interface of a device, a plurality of visualization candidates is generated based on an application of transition rules to the current visualization, a corresponding score for each one of the plurality of visualization candidates is generated based on a corresponding level of data variance for the data of the corresponding visualization candidate, a ranking of the plurality of visualization candidates is generated based on the scores, at least one of the plurality of visualization candidates is selected based on the ranking, and a plurality of selectable visualization suggestions corresponding to the selected visualization candidates is caused to be displayed to the user in the graphical user interface of the device.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: Techniques of providing visualization suggestions are disclosed. In some example embodiments, a current visualization of at least a portion of data of a dataset is determined to be displayed to a user in a graphical user interface of a device, a plurality of visualization candidates is generated based on an application of transition rules to the current visualization, a corresponding score for each one of the plurality of visualization candidates is generated based on a corresponding level of data variance for the data of the corresponding visualization candidate, a ranking of the plurality of visualization candidates is generated based on the scores, at least one of the plurality of visualization candidates is selected based on the ranking, and a plurality of selectable visualization suggestions corresponding to the selected visualization candidates is caused to be displayed to the user in the graphical user interface of the device.

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

Abstract: Described herein are methods and systems for analyzing multidimensional data that use tangential exploration of data via a third or Z-dimension to the current two-dimensional view. The tangential exploration allows higher dimensionality to be explored without causing visual clutter.

Abstract: Described herein are methods and systems for providing hints for and selecting cells within a crosstab that are compatible. Providing hints manifests the compatible cells and the selection is limited to compatible members. Hinting at unselected but compatible cells provides leads for user for other operations they may seek to pursue. User selections that include incompatible cells are corrected to ensure integrity of group operations to be performed on the selections. Compatibility is determined by aggregation level, measure and the like.

Abstract: At least one analytical operation from a set of different analytical operations may be determined based on at least one input. The input(s) may comprise contextual information of working content being displayed to a user on a device and comprising numerical data. Supplemental information for the working content may be generated using the determined analytical operation(s), may comprise a numerical-based analysis of the numerical data, and may be caused to be displayed to the user concurrently with the working content. The contextual information may comprise structured data. The input(s) may further comprise at least one of a history of the user's interactions with the working content, a history of the user's interactions with recommendations of supplemental information for the working content, a history of other users' interactions with the working content, and a history of other users' interactions with recommendations of supplemental information for the working content.

Abstract: Parental dependency information for various data fields may be analyzed to create a data field hierarchy. Each of the data fields may be presented in a selectable list through an interface. Once a data field is selected, any immediate parent(s) and/or child(ren) field(s) of the active data element may be demarcated in the list according to the hierarchy. Additional data entry fields relating to the selected data field and its familial fields may also be displayed. Data in each of data fields may also be analyzed to identify fields with incomplete data. Systems and methods are provided.

Abstract: Various embodiments of systems and methods for generating customizable data visualization are described herein. Several data point generation options may be displayed to visualize a to-be-visualized data element. Data point generation options may then be selected from the displayed data point generation options to render a visualization corresponding to the to-be-visualized data element. Based on the selected data point generation options, the visualization, including data points corresponding to the data values of the to-be-visualized data element, may be rendered.

Abstract: Parental dependency information for various data fields may be analyzed to create a data field hierarchy. Each of the data fields may be presented in a selectable list through an interface. Once a data field is selected, any immediate parent(s) and/or child(ren) field(s) of the active data element may be demarcated in the list according to the hierarchy. Additional data entry fields relating to the selected data field and its familial fields may also be displayed. Data in each of data fields may also be analyzed to identify fields with incomplete data. Systems and methods are provided.

Abstract: Systems and methods to provide filter hints for a result set are provided. In example embodiments, a search request is received and a search is initiated. Documents retrieved from the search are analyzed to determine filters, which may be organized into filter groups. A filter count for each filter is determined. The filter count indicates how many documents match the filter within a current result set or how many additional documents match the filter if selected. The current result set is a set of remaining documents based on any number of previously selected filters from any one or more filter groups being applied thereto. In example embodiments, the filters include enabled filters and any disabled filters, whereby the disabled filters have a zero filter count in the current result set. A visual indicator may also be provided to indicate at least one filter being selected within a collapsed filter group.