Weblog for the AMIS Technology corner
Posts tagged dvt
ADF DVT Hierarchy Viewer on demand – made to order multi table hierachy view
Aug 5th
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 data I received with the email was not entitrely consistent with the mock up of the Hierarchy View.
Tour de France 2011 – Analysis using ADF DVT Graphs – Part 4 – Spark Charts
Jul 27th
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 Chart
Jul 26th
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:
Using the ADF DVT Radar Graph for comparing series – further analyzing ODTUG Kaleidoscope 2011 Session Schedule
Jun 22nd
I have always had a fascination for the Data Visualization capabilities of all the tools and technologies I have worked with. For example: I worked with Oracle Graphics 2.0, back in 1994, and liked it! Fast forwarding through the years – I am now wetting my appetite with ADF’s Data Visualization Tags (DVT) – an impressive array of graphs, gauges, charts and other ways of visualizing data.
One interesting type of graph that I have not actually used before, is the Radar Chart. It is an interesting type of visualization that can plot in one graph values for multiple series against multiple dimensions and make it easy to compare them (the series) with one another. Granted, you can do something similar with a multi-series line chart, yet the comparison is somewhat more pronounced in the radar graph.
See for example the next graph. It plots values for three series – each serie represents a region in the world. For each of the tracks at the ODTUG Kaleidoscope 2011 conference, the percentage of sessions in the track delivered by presenters from the region represented by a serie is plotted. Thus we can see that over 90% of the sessions in the BI and Oracle EPM track is delivered by presenters from the AMERICAS. The Fusion Middleware track has a completely different region-contribution pattern: over 60% of sessions in that track is from EMEA with only a little over 30% coming from the AMERICAS. The relative contributions from the AMERICAS and EMEA for the tracks APEX and Database Development seem fairly similar – both around 30% EMEA can close to 70% from the AMERICAS. The role of APAC is only fairly small at this conference – only contributing to the Fusion Middleware track.
Using ADF 11gR2 DVT component Pivot Table for an on-line analysis of the ODTUG Kaleidoscope session catalog
Jun 20th
ADF comes with a rich collection of component that allow us to visualize and analyze data in ways that previously were only available in fancy OLAP and other BI tools. Now, our own custom developed ADF applications can offer those same fancy capabilities using ADF DVT. Note that Oracle’s BI tools – such as OBI EE – make use of those same components.
This article demonstrates the use of the Pivot Table component – as it is currently shipped in ADF 11gR2. This component presents data in initially very condensed, highly aggregated form and allows the user to ’slice and dice’ and drill down and aggregate along various dimensions.
The Pivot Table is used in this case to analyze the data for the sessions scheduled for the ODTUG Kaleidoscope 2011 conference, later this month, in Long Beach, California. The article will demonstrate that just a few, declarative steps and about 10 minutes of your time are quite enough to include rich analytical capabilities in an ADF application.
The initial Pivot Table shown to the user looks as follows:
ADF 11gR1 – new Hierarchical Viewer for visually pleasing representation of data structures
Jul 7th
One of the data visualization tags required by the teams working on the Oracle Fusion Application Module for Human Resource Management, was a component capable of rendering organization charts. Hierarchical structures from CEO all the way down to the youngest trainee. In a pleasing, graphically interesting, somewhat animated fashion. And so the ADF team developed the Hierarchy Viewer. And since they developed it anyway, we can now make use of it as well. While it may not be the component you will most frequently use, it is certainly an interesting presentation option for special data structures. This component can work against the same tree data binding you would use for tree tables or trees, and can therefore be configured in a very simple, declarative fashion.
In this article some simple examples of how to use this new component. This article is the short summary of a presentation and demonstration I did at the recent ODTUG Kaleidoscope 2009 conference (late June, Monterey). It demonstrates how the conference’s session schedule can be represented in the Hierarchy Viewer.
ADF 11g RichFaces – using a Gauge in a Table to visualize High, Low and Medium Salaries
Nov 5th
One of the sweet spots in ADF 11g RichFaces is of course the library of Data Visualization components. I have written about PivotTable and GanttChart in the past and would now like to also say a few words about Gauges. The collection of Gauges in ADF 11g is quite useful. We will see how easy it is to leverage a Gauge to provide more insight in data.
ADF 11g RichFaces – A closer look at the Pivot Table Data Visualization component
Oct 15th
One of the very interesting JSF components that ADF 11g RichFaces has put in our hands is the Pivot Table. I have blogged about that component a while back (see ADF Faces 11g: Reloading the Matrix – using the Pivot Table component) – and now it is time to take the next step with this component. I have been getting some assistance from the ADF DVT team – Katia’s gang – and with their help I am able to demonstrate some ’second step’ functionality for the pivot table – things that you do not do in the Pivot Table 101, that I described in the previous article.
First we create the Pivot Table – Timesheet – based on a Placeholder Data Control (with static sample data). We will run the page that contains the Pivot Table and see how versatile this already is. We will create a context sensitive context menu that displays additional information about the cell for which it was opened. We will also see how we can add data specific formatting to the cells.
