Posts tagged data visualization
Recently the ADF Special Interest Group at AMIS organized an ADF DVT Speed Date. During this speed date, six ADF specialists from our team presented their favorite Data Visualization Component from the DVT library. In a series of blog posts we share the information with a broader audience. In this post you get introduced to Gantt Charts – which was my own date for this party.ADF DVT Gantt Chart – Introduction
The probably most distinguishing feature of any Gantt Chart is the horizontal time axis. Gantt Charts are used to present data against time.
Gantt Charts were invented in the 1910s by Henry Gantt (hence the name) – although a Polish engineer probably beat him to it but forgot to blog about it (http://en.wikipedia.org/wiki/Gantt_chart). They were used initially for presenting Project Schedules – showing tasks with their start time and end time and their dependencies. The essence of many modern project management tools is in fact a Gantt Chart – frequently an interactive one.
ADF DVT provides this project- and task overview in a Gantt format. Two additional Gantt Charts are provided as well:
In a recent article – Advanced SQL to find valid periods – juggling with outer joins, running totals and analytical functions – I discussed how to use Analytical Functions in SQL to cleverly (!) derive the valid periods from a database table that contains periods of inclusion and exclusion. A valid period is a period for which there is at least one inclusion and for which there is no exclusion. I used several powerpoint based graphics to illustrate the business case. For example:
to depict the periods of inclusion and exclusion and this figure to demonstrate how to derive the valid periods (the blue bars):
After completing this article – and fiddling around in Powerpoint quite a bit – I realized that for visualizing data in a table, I have a perfect tool at my fingertips: the Data Visualization Tags (DVT) in ADF 11g are created for this very purpose: turning data into information through visualization. And this rich library of DVTs components contains – in addition to fairly straightforward visualizations such as bar charts, pie charts and line graphs – also more complex visualization components such as the Bubble Chart, Thematic Map and Gantt Chart. The Gantt Chart has three More >
One of my favorite areas of ADF is Data Visualization. The rich, interactive and (un)usually attractive components that allow me to spice up an ADF application in a very easy straightforward way have a special appeal. We all know that pictures speak volumes. And that a plain table presents data while a carefully designed visualization presents information and perhaps even a call to action. One of my highlights during Oracle Open World 2012 was – not surprisingly – the presentation by the ADF DVT team – Katrina, Hugh and Jairam – together with Yiannis and Vangelis from PCS in Greece who built a wonderful ADF application for private investment management, with beautiful and very effective data visualizations all over the place.
The story of ADF DVT is one that started probably even before ADF with the BI Beans and before that perhaps even with Oracle Graphics. However, forget about all that history and look to the present and the future. No presentation of Fusion Applications is held without showing off its many data visualizations as a means to turn data into information and information into action. Drawing the user to exceptions, deadlines, alerts, patterns and items to act on is More >
This morning, I noticed the following email sitting in my Inbox: “Hello, Lucas.
I found Hierarchy Viewer demo from http://technology.amis.nl/blog/5786/adf-11gr1-new-hierarchical-viewer-for-visually-pleasing-representation-of-data-structures.
But i need to implement some kind of solution. I attached my expected mockup and table structure.
Is it possible? If possible, please suggest me how to do that and put your solution based on your experience.
I’m looking forward to hearing from you soon.”
Obviously, I normall completely ignore such emails – I do have a job you know, and some semblance of a normal life too. However, this email triggered me in some way. And between other more important things, I tried to create the desired hierarchy. The mock up looks like this:
The most challenging part is probably to get the query right. Once that query is defined – in a ViewObject with a selfrefencing ViewLink – creating the hierarchy is very straightforward. Some final styling is required – and a different design to the Radio Buttons, because those are not supported in the Hierarchy Viewer – but otherwise I think I did the job. My end result looks like this (from far away):
Note that the More >
The final episode in a series on ADF DVT applied to the Tour de France 2011 results. In this series, I have used many of the ADF DVT Graph components. In this article, I will use the Spark Chart component to integrate condensed data visualizations inside a table with ‘regular’ data.
The result is a table that lists the top 10 of the final overall ranking of the Tour de France, with for each ride in the top 10 two spark charts:
- the positition in the overall standings at the end of each stage
- the gap with Cadel Evans at the end of each stage (needless to say this chart is fairly pointless for Cadel Evans himself)
The resulting page with the table including the two spark charts looks like this:
Note that the Spark Chart for overall rank displays the #1 position at the top and lower rankings towards the bottom.
We can see from the table how for example the number 10 – Peraud – gradually worked his way up in the ranking – while just as gradually getting further behind Cadel Evans. We also see how Voeckler came to the top somewhere midway and then ever so slightly had to let go of that top position. Only the first three days and the last three days saw him behind Evans.
Tour de France 2011 – Analysis using ADF DVT Graphs – Part 3 – Distance, Speed and Withdrawals with Pareto, Combination, Stock Chart (High/Low) and Bubble Chart0
Another article on analyzing and visualizing the results from the Tour de France 2011 using the ADF DVT components. This article uses the same set of data already discussed in several previous articles – including the standings per stage as well as the overall standings after each stage. This article will focus on using the combination graph, the bubble graph and the high/low (aka stock) chart for taking a closer look at speed, length and withdrawals (and any connection there can be between these aspects).
Some of the pretty pictures created in this article: