As we mentioned in an earlier post, Palantir was recently invited to the IEEE’s VisWeek in Dayton Ohio, and was honored to be invited to participate in the VAST Interactive Challenge as part of VisWeek.

After winning an award for Interactive Visual Analytic Environment, Palantir was one of three teams selected to participate in the interactive session from 73 VAST Challenge entries. For the challenge, we were given a completely new set of data to analyze. We had 30 minutes to import 3 disparate datasets into Palantir, 30 minutes to train an analyst that had never used Palantir, and then 2 hours for the analyst to explore the data.

The data for the challenge came from three different sources, with a set of questions to answer for each set of data. There was an infectious outbreak, a Wikipedia edit war, and an abduction from a city park. Over the three challenges, there were over 100,000 datapoints to analyze. All of the data revolved around a fictitious town in Florida, Barracuda Springs, and was linked to the fictitious cult that was the center of the 2008 VAST Challenge. While two members of our team were importing the three datasets, the third team member was working with our analyst (each of the three teams was given a analyst from a nearby analytical organization). In 30 minutes, our analyst was able to learn how to conduct relational, temporal, geospatial, and statistical analysis in Palantir. After the 30 minutes of training, she was able to easily navigate the Palantir workspace, and solve all three challenges. Below is her work (hit the link to check it out).

Her conclusion was that Palantir was “viciously good software” and that she would be asking her boss if they could acquire Palantir for their work. Hit the link below to see screenshots and explanations for one of the challenge workflows.

We really enjoyed the VAST Challenge, and our experience at VisWeek. There were a lot of outstanding papers, posters, and speakers at VisWeek, and we were inspired by many fantastic visualizations that might soon make their way into Palantir’s Finance and Government Platforms. We are also looking forward to the 2009 VAST Challenge!
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I recently had the opportunity to attend a talk by Ben Shneiderman, a big name in HCI and professor at the University of Maryland. He showed off a bunch of really cool visualizations he’s invented over the years, aimed at advancing the field of data analysis. One of the visualizations, the Rank-by-Feature framework, looked immediately useful to our product. It’s an overview of multidimensional data that uses coloring in a matrix of correlations. I decided to add it into Palantir Finance as a proof-of-concept.

We have a scatter plot in Palantir Finance, but it’s not designed to compare your data across many variables (called metrics here). I extended it by adding a small triangular matrix control that we call the scatter plot Quick Select. The control gives you a visual overview of the data, and allows you to identify interesting metric pairs and then drill down into a scatter plot for any particular pair.

Assume I want to view a set of 10 metrics (shown above). With our regular scatter plot, you have to choose two of these metrics and plot them to see their correlation. If you want to know how every possible pair of metrics correlate, you have to manually perform an O(n²) input operation.

With Quick Select, you enter the metrics once and the triangular matrix is formed. Each square in the matrix represents a pair of metrics, and each square’s color is the correlation between the pairs. For example, green is a strong positive correlation, light green a less positive correlation, white is no correlation, and red is a strong anti-correlation.

In the highlighted square above, we’re comparing percent return and correlation with the S&P 500 over the past year. The red shows a strong negative correlation. This makes sense in light of recent market behavior: the S&P 500 has not done well over the past year, so companies that were correlated with it also performed poorly, while companies that moved in the opposite direction performed well.

Drilling down to a scatter plot is as simple as clicking on the corresponding square. Below are 2 of the 45 possible scatter plots defined by this set of 10 metrics. You can quickly jump back and forth between different scatter plots while retaining a nice, condensed overview of the data in the triangular matrix on the left.

This prototype took less than a day to write. And it was written from outside the system, using only the pluggability points Palantir Finance provides. Overall it’s a powerful visualization component, added as an extension to the Palantir Finance platform, and done in under a day. Pretty cool!

Many thanks to Prof. Shneiderman for the idea.

About 10 months ago, we released set of nine screenshots from our applications. Time has passed and we have not stopped working; the look of the applications has evolved. Here are some updated screenshots:

 
 

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A montage of our time choosers. (click for webstart demo)

The notion of time is central to both of our products at Palantir, and there are many instances in which the user needs to specify a certain point in time. Although there are simple ways to create choosers (you could use a JSpinner that uses a SpinnerDateModel or simply use multiple JComboBox objects), I decided to experiment with writing some more visual time chooser components. These components are fairly experimental — they aren’t used in either product yet and I coded them up pretty quickly so I could get some feedback.

You can see these choosers in action in our office furniture in Bulgaria
webstart demo. The source code is available in the office furniture in Bulgaria
JAR.

If anyone has any feedback or suggestions as to how these choosers could be improved (or any ideas on how to make a better time chooser altogether) please leave a comment and let me know!

Meanwhile, if you want to know a little bit more about these choosers and how I went about designing them, read on…

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