Abstract: Techniques are described for generating geographic borders. In one example, techniques include recognizing a geographic location, determining a starting latitude and a starting longitude and population for the geographic location, and generating a border for the geographic location based on the starting latitude, starting longitude, and population.

Abstract: One or more processors sort a plurality of geometric shapes using one or more size criteria. One or more processors determine whether the plurality of geometric shapes include at least one geometric shape that encapsulates or partially overlaps at least one other geometric shape. One or more processors assign a hierarchical order for the plurality of geometric shapes based, at least in part, on the one or more size criteria and one or more criteria of one or both encapsulation and partial overlap.

Abstract: One or more processors sort a plurality of geometric shapes using one or more size criteria. One or more processors determine whether the plurality of geometric shapes include at least one geometric shape that encapsulates or partially overlaps at least one other geometric shape. One or more processors assign a hierarchical order for the plurality of geometric shapes based, at least in part, on the one or more size criteria and one or more criteria of one or both encapsulation and partial overlap.

Abstract: Techniques are described for generating geographic borders. In one example, techniques include recognizing a geographic location, determining a starting latitude and a starting longitude and population for the geographic location, and generating a border for the geographic location based on the starting latitude, starting longitude, and population.

Abstract: One or more processors sort a plurality of geometric shapes using one or more size criteria. One or more processors determine whether the plurality of geometric shapes include at least one geometric shape that encapsulates or partially overlaps at least one other geometric shape. One or more processors assign a hierarchical order for the plurality of geometric shapes based, at least in part, on the one or more size criteria and one or more criteria of one or both encapsulation and partial overlap.

Abstract: One or more processors sort a plurality of geometric shapes using one or more size criteria. One or more processors determine whether the plurality of geometric shapes include at least one geometric shape that encapsulates or partially overlaps at least one other geometric shape. One or more processors assign a hierarchical order for the plurality of geometric shapes based, at least in part, on the one or more size criteria and one or more criteria of one or both encapsulation and partial overlap.

Abstract: Techniques are disclosed for statistically and ontologically correlated business intelligence (BI) analytics. An example method includes performing an ontological analysis on relevant data defined for a BI analytics query to determine correlations with ontological concepts. The method includes performing a statistical analysis on direct analytics output data to rank the direct analytics output data in order of influence on the direct analytics output. The method includes performing a statistical analysis on the relevant data set relative to the direct analytics output data to determine data in the relevant data set that influence the direct analytics output data, thereby generating a list of key drivers ranked in order of influence. The method includes revising the ranking of the key drivers based on correlations of the key drivers with the ontological concepts. The method includes generating a correlated analytics output comprising information on the key drivers based on the ranking.

Abstract: Techniques are disclosed for statistically and ontologically correlated business intelligence (BI) analytics. An example method includes performing an ontological analysis on relevant data defined for a BI analytics query to determine correlations with ontological concepts. The method includes performing a statistical analysis on direct analytics output data to rank the direct analytics output data in order of influence on the direct analytics output. The method includes performing a statistical analysis on the relevant data set relative to the direct analytics output data to determine data in the relevant data set that influence the direct analytics output data, thereby generating a list of key drivers ranked in order of influence. The method includes revising the ranking of the key drivers based on correlations of the key drivers with the ontological concepts. The method includes generating a correlated analytics output comprising information on the key drivers based on the ranking.

Abstract: A processor-implemented method for generating a chart specification is provided. The method may include identifying at least one chart component within a plurality of chart components associated with an image of a chart. The method may also include determining whether the at least one chart component meets a threshold score. The method may further include identifying elements associated with the at least one chart component when the at least one chart component is determined to meet the threshold score. Additionally, the method may include generating a chart specification based on the identified elements.

Abstract: A processor-implemented method for generating a chart specification is provided. The method may include identifying at least one chart component within a plurality of chart components associated with an image of a chart. The method may also include determining whether the at least one chart component meets a threshold score. The method may further include identifying elements associated with the at least one chart component when the at least one chart component is determined to meet the threshold score. Additionally, the method may include generating a chart specification based on the identified elements.

Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to receive input data, and determine, based at least in part on the input data, a plurality of visualizations, each of the plurality of visualizations representing at least a portion of the input data. The at least one module is further operable to determine a respective score for each of the plurality of visualizations, the respective score for each visualization from the plurality of visualizations being based at least in part on a comparison of the corresponding visualization to one or more visual patterns, determine, based at least in part on the respective score for each of the plurality of visualizations, an ordering of the plurality of visualizations, and output, for display, at least one visualization from the plurality of visualizations in accordance with the ordering.

Abstract: In one example, a method includes identifying a plurality of data representation features comprised in each of one or more dashboards. The method further includes identifying formatting data for a format of the data representation features in each of the one or more dashboards. The method further includes generating one or more thumbnail images corresponding to each of the one or more dashboards, wherein each of the one or more thumbnail images comprises a plurality of stock images representative of a corresponding one of the data representation features in the corresponding dashboard, in a format that represents the format of the corresponding dashboard.

Abstract: Techniques of the disclosure may include a method comprising receiving a first request for a recommendation to configure input data for output, determining, based at least in part upon templates, an object class corresponding to the first request, determining, based at least in part on input data and the templates, one or more output objects and one or more scores, wherein the output objects comprise configurations of the input data for the determined object class, and where each of the output objects is associated with a score. The method may further comprise outputting a indication of the output objects having an associated score that exceeds a specified threshold value, responsive to receiving a second request to resolve a specified output object, determining, based at least in part on the one or more templates and the second request, a resolved output object and outputting an indication of the resolved output object.

Abstract: Techniques of the disclosure may include a method comprising receiving a first request for a recommendation to configure input data for output, determining, based at least in part upon templates, an object class corresponding to the first request, determining, based at least in part on input data and the templates, one or more output objects and one or more scores, wherein the output objects comprise configurations of the input data for the determined object class, and where each of the output objects is associated with a score. The method may further comprise outputting a indication of the output objects having an associated score that exceeds a specified threshold value, responsive to receiving a second request to resolve a specified output object, determining, based at least in part on the one or more templates and the second request, a resolved output object and outputting an indication of the resolved output object.

Abstract: In one example, a method includes identifying a plurality of data representation features comprised in each of one or more dashboards. The method further includes identifying formatting data for a format of the data representation features in each of the one or more dashboards. The method further includes generating one or more thumbnail images corresponding to each of the one or more dashboards, wherein each of the one or more thumbnail images comprises a plurality of stock images representative of a corresponding one of the data representation features in the corresponding dashboard, in a format that represents the format of the corresponding dashboard.