Abstract: In an approach to improve generating an analytic application embodiments generate an analytic application from an image. Embodiments determine an orientation, font size, and color of a text, wherein the text is selected from one or more texts that are part of a visualization from the image. Embodiments classify the text using semantic analysis having predetermined criteria to create a result. Furthermore, embodiments bind one or more columns from a dataset to the visualization using the result of the semantic analysis, and apply a selection identified using predetermined criteria, comprising sorting, filtering, grouping, and aggregating, to data of the visualization. Additionally, embodiments create a new color palette having a dominant color in the analytic application, wherein the dominant color is identified as a most recurring color in the visualization of the image, and apply the new color palette to the analytic application.

Abstract: A computer-implemented method, system and computer program product for generating a visual transformation of a business analytic artifact. Features from a received business analytic artifact (e.g., dashboard) are extracted. Furthermore, constraints are determined for a transformed representation of the business analytic artifact. A heuristic-based search is then performed on a space of possible transformed representations of the business analytic artifact using the extracted features to identify a transformed version that satisfies the determined constraints and maximizes readability. A final score is then generated for each possible transformed representation of the business analytic artifact based on the extent that the transformed version satisfies the determined constraints and maximizes the readability. The transformed representation of the business analytic artifact that has the highest score is selected and displayed on the target device.

Abstract: In an approach to improve generating an analytic application embodiments generate an analytic application from an image. Embodiments determine an orientation, font size, and color of a text, wherein the text is selected from one or more texts that are part of a visualization from the image. Embodiments classify the text using semantic analysis having predetermined criteria to create a result. Furthermore, embodiments bind one or more columns from a dataset to the visualization using the result of the semantic analysis, and apply a selection identified using predetermined criteria, comprising sorting, filtering, grouping, and aggregating, to data of the visualization. Additionally, embodiments create a new color palette having a dominant color in the analytic application, wherein the dominant color is identified as a most recurring color in the visualization of the image, and apply the new color palette to the analytic application.

Abstract: In an approach to combining visualizations in a business analytic application, responsive to receiving a plurality of original visualizations from a user, one or more visualization groups are created. A strength score is calculated for each visualization group of the one or more visualization groups. Responsive to the strength score for each visualization group of the one or more visualization groups meeting a minimum threshold score, one or more recommended visualizations are generated, wherein the one or more recommended visualizations are combinations of the plurality of original visualizations. The one or more recommended visualizations are sent to the user.

Abstract: In an approach to combining visualizations in a business analytic application, responsive to receiving a plurality of original visualizations from a user, one or more visualization groups are created. A strength score is calculated for each visualization group of the one or more visualization groups. Responsive to the strength score for each visualization group of the one or more visualization groups meeting a minimum threshold score, one or more recommended visualizations are generated, wherein the one or more recommended visualizations are combinations of the plurality of original visualizations. The one or more recommended visualizations are sent to the user.

Abstract: Provided is a method for extracting features from an image of a dashboard. The method comprises detecting a position of one or more visualizations in an image of a dashboard. Each of the one or more visualizations is classified based on a type of object in the visualization. Features of the visualizations are extracted. The features include data points underlying the visualizations, one or more colors in the image, and text found in the image. An output array is generated based on the extracted features.

Abstract: A computer-implemented method, system and computer program product for recommending a template or a dataset. A descriptor (input descriptor) of the input, corresponding to a dataset or a dashboard template, is extracted or fetched. The input descriptor may be the descriptor of the dataset which includes topics and concept combinations. Alternatively, the input descriptor may be the descriptor of the dashboard template which includes topics, concept combinations and column-to-visualization mappings. Scores are then generated for the candidate targets, corresponding to dashboard templates or datasets, based on one or more of the following: matching topics in the input descriptor with topics in the candidate target descriptors, matching concept combinations in the input descriptor with concept combinations in the candidate target descriptors, and determining compatibility of the datasets with the dashboard templates.

Abstract: A computer-implemented method, system and computer program product for creating a descriptor for a dashboard template. The column-to-visualization mappings are extracted from a dashboard of a created or modified dashboard (or a created or modified dashboard template). Furthermore, the concept combinations from each visualization of the dashboard are extracted. Additionally, topics from the dashboard are extracted. The concept combinations, topics and column-to-visualization mappings are aggregated into a dashboard template descriptor. The dashboard template descriptor is then stored. In this manner, the dashboard template descriptor captures how concept combinations are used in the visualizations of the dashboard as well as how high-level concepts (topics) are incorporated in the dashboard. Furthermore, the dashboard template descriptor captures how the concepts of the columns of a dataset are mapped to the visualizations of the dashboard.