One of the things which has bothered me ever since I took the dive into visualisation is the problem of interactivity. The aim of interacting with a visualisation is to drill down or explore areas of the visualisation which are (or seem) interesting. Put another way, we are essentially filtering the data from a visual standpoint. In most cases, mouse interactions may be sufficient. But what if I wanted to be able to filter the data programmatically and have the result reflected in the visualisation?
One way is to simply re-run the code which generates the visualisation each time we use a different filter. This is the simplest, and, in many cases, enough. In this case, the modification to the code is made in an offline fashion. What if we wanted to do the same, but while the program is running? This describes my attempt at one such implementation. Albeit still somewhat primitive, we’ll see where it ends up. For the purposes of demonstration, I used the Parallel Coordinates visualisation, which is available on GitHub. I’ll continue using Processing through Ruby-Processing for this description. Continue reading Driving data visualisation over a queue using RabbitMQ and lambda-queuer→
This is an update on the visualisation post on parallel coordinates. Understanding the Processing model made me realise that it probably wasn’t a good idea to draw all the samples each time draw() was called. Of course, every refreshed call of draw() does not clear away the previous frame’s graphics, so that just makes it easier. In the end, I went and explicitly drew only the samples which were under the current mouse position.
The speedup is obvious and massive: whereas the previous version worked well with only 300 samples, the current one processes 18000 samples without breaking a sweat. At 29,000 samples, there is a bit of a slowdown, but only just a bit, you wait 1 second for the highlighting instead of 6-7.
Here’s the new video, using 18k samples. Notice how much denser the mesh is.
Processing is growing on me. Inspired by the different and (very) interesting data visualisation examples I’ve seen, I decided to take a shot at interacting with the parallel coordinates that I generated here. Of course, I had to reduce the number of samples for this demonstration; it’d slow to a unholy crawl otherwise. For this video, I’ve taken 300 samples. The interaction is essentially a mouse-hover highlighting of any sample(s) under it. I fiddled with the colors a bit, but decided that a white-on-greyscale scheme would show up better.
Of course, I still haven’t gotten around to labeling the axes. This I’ll probably pick up next. But as the video demonstrates, there’s a lot to Processing than meets the eye.
PS: By the way, the actual demonstration ends around the halfway mark; I was trying to figure out how to stop the bloody recorder.
This is the visualisation of the same dataset that I’ve been working on for a while, for exploring different data mining and visualisation techniques. Currently, the axes aren’t labelled, and the color coding is for different categories. Looks like a really interesting way to explore the data.
I should have seen it coming. Text communicates well – up to a point. All the current analyses I’ve been working on, starting from Self-Organising Maps to Decision Trees, are very well served by good, solid visualisation. My current need is a way to visualise data structures effectively, even if it is merely a bunch of nodes which can be expanded/collapsed to show more information. Additionally, it would be nice (not necessary) for this to happen interactively, but I don’t mind a command line-driven approach. In fact, I prefer the command line; makes it easier to drive it through a scripting language like Ruby.
So far, I’ve looked at Processing, d3.js and ProtoVis. I like the idea of d3.js: the data-driven approach makes a lot of sense, but I think I need to refresh quite a bit of CSS-fu to take advantage of its capabilities. Apart from visualisation derived from data mining algorithms, showing the data as-is in an aesthetic manner is also a worthy goal at this point. In particular, the parallel coordinates visualisation caught my eye.
Oh well, at least I know what I’ll be doing for the next few days.