FEATURE EXTRACTION FROM DASHBOARD VISUALIZATIONS

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.

BACKGROUND

The present disclosure relates generally to the field of computing, and more particularly to extracting features of a dashboard from its images.

A dashboard is a type of graphical user interface which often provides at-a-glance views of key performance indicators (KPIs) relevant to a particular objective or business process. In other usage, “dashboard” is another name for “progress report” or “report.” The “dashboard” is often displayed on a web page which is linked to a database that allows the report to be constantly updated. For example, a manufacturing dashboard may show numbers related to productivity, such as number of parts manufactured or number of failed quality inspections per hour. Similarly, a human resources dashboard may show numbers related to staff recruitment and retention.

SUMMARY

Embodiments of the present disclosure include a method, computer program product, and system for extracting features from an image of a dashboard, including from the visualizations contained within the image. 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 extracted 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.

DETAILED DESCRIPTION

Aspects of the present disclosure relate generally to the field of computing, and in particular to extracting features from an image of a dashboard, including visualizations contained within the image. While the present disclosure is not necessarily limited to such applications, various aspects of the disclosure may be appreciated through a discussion of various examples using this context.

Building a business analytics application, such as an application that provides a dashboard of KPIs for a business, can require a large amount of time and effort. Once the business analytics application is built, users may be hesitant to change the underlying architecture or platform that the application uses. One of the challenges that new customers face is migrating their existing business analytic application over to a new system, which may not be compatible with the original system. As such, the applications cannot simply be moved to the new system; they often have to be completely rebuilt. There are no migration tools for dashboards or reports that enable migration between, for example, Microsoft® Power BI®, Tableau®, or IBM Cognos® Analytics. In some cases, users also face the same dilemma if they want to migrate from older versions of a business analytics system to newer ones, or even if they want to convert from a report to a dashboard or vice versa.

Embodiments of the present disclosure provide a mechanism to enable the migration of a dashboard from one system to another, from one version of the system to another, and/or from a report to a dashboard or vice versa. Provided is a method for feature extraction for an image of a business analytic application (e.g., dashboard and/or report). The feature extraction may be performed automatically by, for example, identifying information about one or more visualizations found in the dashboard or report. The identified information may include the underlying data for each visualization in the dashboard or report (e.g., underlying data for a chart or graph), as well as the layouts, colors, and/or text used in each of the visualizations. This information may be extracted from an image (e.g., a screenshot) of a business analytics application (e.g., dashboard). In some embodiments, these features are extracted for the purpose of recreating the dashboard/report in a different business analytic tool (e.g., IBM Cognos® Analytics).

In some embodiments, the method includes the following operations: detecting the position of each visualization, classifying the type of each visualization, extracting the data points underlying the visualizations, extracting the color palette of the application, and identifying any text found in the image, including bounding boxes. These operations may be performed in various orders, with some operations performed simultaneously and others performed sequentially.

As used herein, a visualization may refer to a discrete portion of the dashboard (and, accordingly, a portion of the image) that contains or depicts a visual object, such as a chart, a graph, a table, etc. Accordingly, an image of a dashboard may contain multiple visualizations; one corresponding to each object in the dashboard. For example, a dashboard may include two different graphs. As such, an image of this dashboard can be said to include two visualizations, one corresponding to each graph. Embodiments of the present disclosure can be applied to dashboards having any number of objects and corresponding visualizations.

Because a visualization is a portion of the dashboard (or a portion of an image of the dashboard) that contains or depicts a visual object, the terms visualization and object are used in similar manners and/or interchangeably herein, except where it is clear from the context that they are describing different things. For example, “classifying the type of a visualization” and “classifying a type of an object” may refer to the same concept, namely determining the type of information (e.g., line graph, bar graph, pie chart, etc.) displayed in a given portion of the dashboard (e.g., depicted by the visualization). So if, for example, the visualization is of a pie chart, the “type” of the visualization and the “type” of the object are the same thing; namely, a pie chart. Similarly, if the visualization is of a line graph, “extracting the data points underlying the visualizations” may include determining the values of the data points of the object depicted in the visualization (e.g., in the line graph), and may accordingly be referred to as extracting the data points of the object.

Two example methods will now be discussed with reference toFIGS. 1 and 2, according to embodiments of the present disclosure. Referring first toFIG. 1, illustrated is a flowchart of an example method100for extracting features from an image of a dashboard, in accordance with embodiments of the present disclosure. The method100may be performed by hardware, firmware, software executing on a processor, or any combination thereof. The method100may begin at operation102, wherein an input image is received. In at least one embodiment of the present disclosure, the received image is an image of a dashboard, report, or other business analytic application that is input by a user.

For example, at operation102, the user may input one or more images into a system for extracting features of a dashboard for migrating a dashboard from one business analytic environment to another. Each image may be, for example, a screenshot of a dashboard or report, and can have any suitable format (e.g., .jpeg, .png, .jpg, .gif, .tiff, etc.). Each image may relate to a different view of the dashboard. For example, a dashboard may include one or more tabs that, when selected, switch to a different “page” of the dashboard. Each image may pertain to one of such tabs. A user may click through the various tabs or other page-changing elements and create an image for each “page.”

In some embodiments, the system may be configured to generate the one or more images itself. For example, the user could select a dashboard that is going to be migrated, and the system may generate a plurality of images for the dashboard (e.g., one image for each view of the dashboard). In at least one embodiment of the present disclosure, the system could generate the plurality of images by identifying clickable elements (e.g., tabs for different pages, etc.) in the dashboard that change the view of the dashboard and then selecting each element to flip through the various views (e.g., the tabs). In embodiments where the user (or the system) selects, generates, or otherwise inputs multiple images, operations104-116may be performed for each image.

At operation104, the system uses a custom object detection model to detect one or more visualizations (e.g., depicting objects such as charts, tables, graphs) in the image. In at least one embodiment of the present disclosure, detecting the one or more visualizations includes detecting a location of each visualization in the image. The term “location” and the term “position” are used interchangeably herein. The object detection model may be trained specifically to detect objects (e.g., charts, graphs) in dashboards and business analytics reports using image/object analysis and recognition. The various objects are detected based on analysis of the dashboard image. The customized object detection model may be based on a convolutional neural network that is trained to differentiate between various visualizations/objects in the image and detect their locations.

Accordingly, at operation104, the object detection model is applied to process each input image of a business analytic application (dashboard or report) and detect the location of each visualization in the application. In some embodiments, this customized detection model can be created using the Watson® Visual Recognition Service. The customized object detection model is trained on a dataset consisting of images of dashboards and/or reports. All of the types of visualizations in the dataset are labeled with the same detection class. Accordingly, the custom object detection model is trained to detect every visualization, but it does not need to differentiate between types of visualizations (e.g., bar chart vs. line graph). This novel training method reduces the learning difficulty of the object detection model without the need to modify the model itself. Therefore, the performance of the model is improved compared to training the model with one class per visualization type. The output from the object detection model is the identification of the objects (and associated visualizations) and their relative locations on the dashboard.

At operation106, each visualization (e.g., for each detected object) is “cut” out of the image and separated for individualized analysis. For example, after visualizations have been detected at operation104, they are cut out of the input image using the detected locations. In at least one embodiment of the present disclosure, locations are detected using bounding boxes that define the edges of the visualization. Each extracted, or cut out, visualization can then be treated individually as a separate image. The individual visualizations are then processed to extract feature information from them. Feature information may include, by way of non-limiting example, color, type, underlying data, and/or text. Each visualization may be processed at one or more of operations108-114, which may be performed partially or wholly simultaneously, partially or wholly sequentially, and/or in various orders.

At operation108, the system uses a color extraction method to extract one or more dominant colors present in the visualization. Any color extraction model that is able to detect the dominant colors used in an image may be applied to the image of the dashboard/report to extract one or more dominant colors present in the visualization. The dominant colors extracted from the image can then be used to create a color palette for the migrated or new dashboard. Accordingly, by extracting the dominant colors from the image to create the color palette, the migrated or new dashboard can be made to look like the original dashboard depicted in the image that the user input at operation102.

In some embodiments, the color palette extraction process can be applied to each visualization individually, to two or more visualizations as a set, on the dashboard/image level, or any combination thereof. In other words, the system may determine the dominant colors for the dashboard as a whole, for a subset of the visualizations, for each visualization separately, or any combination thereof. In order to perform visualization-level analysis, the color extraction model may use the location data determined at operation104to analyze each visualization separately. Extracting out color palettes at multiple granularity levels enables the user to be provided more options when generating the migrated or new dashboard. In such embodiments, the user is then able to select a color palette that he or she prefers.

At operation110, the system uses one or more custom object classification models to classify the type of object represented by the visualization. In these embodiments, the visualizations may be fed into a custom image classification model based on a convolutional neural network (e.g., that performs object classification). In some embodiments, this custom classification model can be created using the Watson® Visual Recognition Service. The model classifies each visualization as one of the pre-defined types such as bar chart, pie chart, line chart, etc. The custom image classification model is trained with a dataset consisting of images of individual visualizations having a variety of known types.

At operation112, the system extracts the underlying data points and other information from the visualization. The extracted data may include values for various parameters, as well as names of those parameters (e.g., column names). The extracted data points could be stored in a table or other array. In some embodiments, the data point extraction tool can be any tool that is able to analyze the visualization and then generate a data table from the visualization.

In some embodiments, the system may select from a plurality of data point extraction tools or models. For example, the system may include a plurality of models for extracting data points from different types of visualizations. Using the output from operation110(i.e., the type of visualization), the system may select an appropriate model to use when extracting out the data values. This may be done, for example, based on the accuracy or efficiency of the models at extracting data from certain types of visualizations. For example, the system may use a first model to extract information from pie charts and a second model to extract information from bar graphs. This may be done due to the computational efficiency and/or accuracy of the models (e.g., the first model is better at extracting data from pie charts than the second model, but the second model is better than the first at extracting data from bar graphs). Any number of suitable models may be used, and their selection may be based on system resources, efficiency of the models, accuracy of the models, other factors recognized by persons of ordinary skill in the arts, or any combination thereof.

At operation114, the system uses optical character recognition to extract text from the visualization and/or the image. In at least one embodiment of the present disclosure, extracting text includes detecting the location and value of text in the visualization and/or the image. In at least one embodiment of the present disclosure, the system may utilize an Optical Character Recognition (OCR) model (e.g., the tesseract library) to extract the text. In at least one embodiment, the text is extracted from the image of a dashboard/report. This can include text that is in a visualization. In at least one embodiment, the text is extracted from an individual visualization. In such embodiments, the model identifies the text and the position of the text in the visualization. Extracted text may include, but is not restricted to: the dashboard/report title, the titles of individual tabs, the title and axis labels of each visualization, and any text widget content.

In some embodiments, the OCR model detects the text and its bounding box (its position, e.g., pixel location), as well as additional information about the text. The additional information may include, but is not limited to, one or more of: its height (e.g., Y pixels tall); its width (e.g., X pixels wide); its directionality/orientation, such as whether the text is displayed normally in a left-to-right fashion, whether it is displayed vertically (e.g., rotated 90°), or whether it is rotated by some other amount (e.g., 45°); and/or its color.

At operation116, the system aggregates the feature information extracted from each visualization and from each extraction operation (e.g., operations108-114) applied to each visualization. The system uses the aggregated feature information to generate a logically grouped specification (e.g., in the form of an array). The logically grouped specification includes information about the visualizations that enables the system (or other systems) to generate a comparable, representative visualization. In other words, the feature information that was extracted and determined for the visualizations enables the generation of a new dashboard, in the same or different business analytics platform, that contains the same information and/or user experience (UX), or feel, as the original dashboard. This allows for the effective migration of the dashboard from one system to another.

Referring now toFIG. 2, illustrated is a flowchart of a second example method200for extracting features or feature information from a dashboard, in accordance with embodiments of the present disclosure. The method200may be performed by hardware, firmware, software executing on a processor, or any combination thereof. The method200may include the same operations as the method100, just arranged in a different order. As such, details about the individual operations are omitted for brevity.

In the method200, an image is received at operation202. For example, at operation202, a user may upload an image of a dashboard. This may be done in substantially the same way as described with respect to operation102of method100(shown inFIG. 1).

The visualizations, including the location of each visualization, in the image may be detected at operation204. This may be done in substantially the same way as described with respect to operation104of method100.

The system may then classify the visualizations at operation206. The classification may indicate the type of object depicted in the visualizations (e.g., bar graph, pie chart, etc.) This may be done in substantially the same way as described with respect to operation110of method100. At this point, the system has information pertaining to a set of visualizations, a location of each visualization, and a type of each visualization.

The system may then extract data points from each visualization at operation208. This may be done in substantially the same way as described with respect to operation112of method100.

At substantially the same time that one or more of operations204-208are being performed, the system may extract text from the image at operation210. This may be done in substantially the same way as described with respect to operation114of method100.

At substantially the same time that one or more of operations204-208and/or operation210are being performed, the system may extract dominant colors from the image and generate a color palette of the dominant colors at operation212. This may be done in substantially the same way as described with respect to operation108of method100.

The outputs from each of operations208,210, and212(i.e., various features of the image) may be aggregated at operation214. This may be done in substantially the same way as described with respect to operation116of method100.

In some embodiments, the system may be configured to effectively perform one or more of five discrete operations:

1. Detect the locations of visualizations in a business analytic application

2. Classify the visualizations into chart types

3. Extract text from the images and/or visualizations

4. Extract one or more color palettes from the images and/or visualizations

5. Extract data points from the visualizations.

An example application of one or more embodiments of the present disclosure will now be described with reference toFIGS. 3-5. Looking first atFIG. 3, illustrated is an example dashboard300in which embodiments of the present disclosure may be implemented.

The dashboard300is a dashboard showing sales data for an Ouro Brasileiro espresso and ginger scone promotion. The dashboard includes five discrete objects. The first object302displays sales performance by week using a bar graph. The second object304displays the monetary value of the spoilage the week before the promotion. The third object306displays the monetary value of the spoilage during the promotion week. The fourth object308displays the ginger scone spoilage quantity over the course of 16 days using a line graph. The fifth object310displays the sales performance by day using a bar graph. Each of the five objects is associated with its own visualization.

A user of the disclosed system takes an image of the dashboard300and feeds it into the system. This is an example of the performance of operation102of method100. The system then uses a custom object detection model to automatically detect the five objects302-310(and the five corresponding visualizations) in the dashboard300. This is an example of the performance of operation104of method100. The system then “cuts” out each visualization and performs feature extraction processes on each visualization. An example of a visualization that could be cut out is shown inFIG. 4.

Referring now toFIG. 4, illustrated is a visualization400that has been extracted from a dashboard, in accordance with embodiments of the present disclosure. The visualization400is a bar chart representing the average price of some good or service in the five boroughs of New York City. After the visualization400is cut out of the larger image, it can be processed such that various features are extracted from it. This processing may include one or more of the operations108-114described with respect toFIG. 1.

For example, as shown inFIG. 5, the system may extract chart data points from the visualization400. This may be done as described with respect to operation112. In the example shown inFIGS. 4 and 5, the system analyzes the visualization400and extracts out the text (e.g., Bronx, Brooklyn, etc.) and corresponding values. The text and values may then be correlated into an array, table, or other data storage form500. For example, as shown in the first column510of the table500, the system has determined that the X-axis represents the “neighborhood group” and includes Bronx, Brooklyn, Manhattan, Queens, and Staten Island. Similarly, the system has determined that the Y-axis represents the price, as shown in the second column520of the table500. The system has further analyzed the visualization400to determine the value for each borough based on the size of the associated bars in the visualization400. For example, the system has determined that the price of the good or service averages $87 in the Bronx, $124 in Brooklyn, $196 in Manhattan, $100 in Queens, and $114 in Staten Island based on image analysis of the visualization400.

While not shown in the figures, the system may also extract additional features about the visualization400, such as the color (operation108). Once all of the relevant features have been extracted, the system may aggregate the features and generate an array or other output (e.g., a specification) that includes the features in a logically ordered way. This output may then be used to generate a new dashboard in a different system, where the new dashboard is substantially similar to the original dashboard.

The following is an example of the output array of this dashboard feature extraction as applied to the visualization400:

As can be seen from this output array, the type of chart (column) and values for each of the boroughs, as well as the borough names, are included. Similarly, the minor and major gridlines (which are shown every $20 increase in price) are also included in the output array. Furthermore, information such as the size of the font, the color of the visualization400, and the axis titles are included. This information allows a system to rebuild the dashboard without tedious, manual work.

Each object in the dashboard may have its own “widget” in the output array. For example, while the above output array includes information for the visualization400inFIG. 4, other visualizations in the dashboard (not shown) would have their features extracted as well. As such, the output array would also include the information for each of those other visualizations. For example, the output array of this dashboard feature extraction as applied to a pie chart type visualization from the same image (not shown) may also include the following:

In this way, each input dashboard may have a single output array that includes the features of every visualization found in that dashboard. In some embodiments, a single dashboard may have multiple output arrays (e.g., one for each “view” or “page,” one for each visualization, etc.).

In some embodiments, the system uses the output array to generate a new dashboard. In some embodiments, the system exports the generated output array (e.g., to another application on the system, to another system, etc.) for generating the new dashboard. For example, the system may be a cloud-based system that is configured to export the output array to another system in the cloud which hosts the dashboard.

Referring now toFIG. 6, shown is a high-level block diagram of an example computer system601that may be used in implementing one or more of the methods, tools, and modules, and any related functions, described herein (e.g., using one or more processor circuits or computer processors of the computer), in accordance with embodiments of the present disclosure. In some embodiments, the major components of the computer system601may comprise one or more CPUs602, a memory subsystem604, a terminal interface612, a storage interface616, an I/O (Input/Output) device interface614, and a network interface618, all of which may be communicatively coupled, directly or indirectly, for inter-component communication via a memory bus603, an I/O bus608, and an I/O bus interface unit610.

The computer system601may contain one or more general-purpose programmable central processing units (CPUs)602A,602B,602C, and602D, herein generically referred to as the CPU602. In some embodiments, the computer system601may contain multiple processors typical of a relatively large system; however, in other embodiments the computer system601may alternatively be a single CPU system. Each CPU602may execute instructions stored in the memory subsystem604and may include one or more levels of on-board cache.

System memory604may include computer system readable media in the form of volatile memory, such as random access memory (RAM)622or cache memory624. Computer system601may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system626can be provided for reading from and writing to a non-removable, non-volatile magnetic media, such as a “hard drive.” Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), or an optical disk drive for reading from or writing to a removable, non-volatile optical disc such as a CD-ROM, DVD-ROM or other optical media can be provided. In addition, memory604can include flash memory, e.g., a flash memory stick drive or a flash drive. Memory devices can be connected to memory bus603by one or more data media interfaces. The memory604may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments.

One or more programs/utilities628, each having at least one set of program modules630may be stored in memory604. The programs/utilities628may include a hypervisor (also referred to as a virtual machine monitor), one or more operating systems, one or more application programs, other program modules, and program data. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules630generally perform the functions or methodologies of various embodiments.

Although the memory bus603is shown inFIG. 6as a single bus structure providing a direct communication path among the CPUs602, the memory subsystem604, and the I/O bus interface610, the memory bus603may, in some embodiments, include multiple different buses or communication paths, which may be arranged in any of various forms, such as point-to-point links in hierarchical, star or web configurations, multiple hierarchical buses, parallel and redundant paths, or any other appropriate type of configuration. Furthermore, while the I/O bus interface610and the I/O bus608are shown as single respective units, the computer system601may, in some embodiments, contain multiple I/O bus interface units610, multiple I/O buses608, or both. Further, while multiple I/O interface units are shown, which separate the I/O bus608from various communications paths running to the various I/O devices, in other embodiments some or all of the I/O devices may be connected directly to one or more system I/O buses.

It is noted thatFIG. 6is intended to depict the representative major components of an exemplary computer system601. In some embodiments, however, individual components may have greater or lesser complexity than as represented inFIG. 6, components other than or in addition to those shown inFIG. 6may be present, and the number, type, and configuration of such components may vary. Furthermore, the modules are listed and described illustratively according to an embodiment and are not meant to indicate necessity of a particular module or exclusivity of other potential modules (or functions/purposes as applied to a specific module).

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

It is to be understood that the aforementioned advantages are example advantages and should not be construed as limiting. Embodiments of the present disclosure can contain all, some, or none of the aforementioned advantages while remaining within the spirit and scope of the present disclosure.

When different reference numbers comprise a common number followed by differing letters (e.g.,100a,100b,100c) or punctuation followed by differing numbers (e.g., 100-1, 100-2, or 100.1, 100.2), use of the reference character only without the letter or following numbers (e.g., 100) may refer to the group of elements as a whole, any subset of the group, or an example specimen of the group.

In the foregoing, reference is made to various embodiments. It should be understood, however, that this disclosure is not limited to the specifically described embodiments. Instead, any combination of the described features and elements, whether related to different embodiments or not, is contemplated to implement and practice this disclosure. Many modifications, alterations, and variations may be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. Furthermore, although embodiments of this disclosure may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of this disclosure. Thus, the described aspects, features, embodiments, and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Additionally, it is intended that the following claim(s) be interpreted as covering all such alterations and modifications as fall within the true spirit and scope of the invention.