How to Configure the Gantt Chart Plugin from Oracle’s BI Public Store for Oracle Data Visualization Desktop (DVD)

May 3, 2017

Author: Astha Patni, Performance Architects

Oracle’s Data Visualization Desktop (DVD) supports custom visualizations to satisfy the requirements of developers and end users. These plugins are available on Oracle’s BI Public Store. Within this website, Oracle provides a variety of data analysis and visualization plugins that can be installed on a user’s local client installation.  Once installed, these plugins are immediately visible within DV Desktop.

Oracle BI Public Store

In this example, we will add a new visualization called “Gantt Chart” into DV Desktop and demonstrate its features and options.  The Gantt chart visualization is a type of chart that illustrates the breakdown of a project into its tasks and sub-tasks. This chart illustrates the start, end, and duration of tasks within a project. The Gantt chart is one of most commonly used visualization to depict the timeline for tracking projects.

The process of installing the plugin begins by clicking on the “Gantt Chart” icon in the Oracle BI Public Store.  This will display a brief description of the plugin and a link to download the zip file.

After the download completes, the zip file is copied to a plugin directory under the user’s DV Desktop local application directory.   For example, for the Administrator user, the directory would be:


Once the plugin zip file has been copied to the above directory, start or restart DV Desktop and the “Gantt Chart” visualization will be immediately available for use in new or existing DV Projects.

To demonstrate the Gantt chart, we will use a sample “Project Management” Excel sheet as a data source. The spreadsheet has information about the different projects implemented in an organization. Below is the sample data:

The first step is to import the data source into DVD. Create a new data source and browse the Excel spreadsheet using the “File” option.

Before accepting the data set, validate the data type of all the fields. In this case, “Resource” and “Cost” were coming as attributes, so they are changed to “Measure” for aggregation purposes. You can also change the metadata after import in case it is missed in this step. Click “OK” to accept the below data set.

Create a new project using the “Project Management” data source. Select these three fields: “Start Date,” “End Date” and “Project Name.”  It is important to select three fields including two dates and one attribute to prevent display issues with the “Gantt Chart” visualization.

The resulting visualization shows the attribute field spans across the start date and end date.  In this example, Project 1 is extended across the duration of 116 days, which the difference between the minimum start date of the task “Development” and the maximum end date of the task “Testing” against Project 1. Also, “Task” is not yet included in the chart so only a single bar can be seen against each project.

Add the metrics in the “Values” section. In the example below, the two metrics are “Resource” and “Cost.” Measures at the “Task” level as per the data set are summed up to the “Project” level. To show the summed-up value, metrics are identified with “**sum.”

Validation with the data source confirms that “Resources” involved in Project 1 are 37 and “Cost” is $44,000.

The only properties that can be changed on the “Gantt Chart” view are the major axis and minor axis. These can be changed to years, quarters, months, weeks, or days. Other properties are common to all the other visualizations like auto fit, canvas properties, etc.

Next add the “Task Name” to the “Color” section. This splits all of the projects into their respective tasks showing that each project has multiple tasks. Each task under a project is represented by a bar with metrics on it. For example, one of the bars for Project 1 shows “** sum” for the metrics as the same task “Development” has two rows in the datasource.

In conclusion, it can be useful to study the different type of visualizations available in the Oracle BI Public Store to determine which ones are best suited to view a certain data set. No complex configuration is required for these visual images, and these display the data in constructive ways.  It is worthwhile to have a weekly glance at the Oracle BI Public Store to be up-to-date on the different visualizations introduced in the Oracle BI world.

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