A clever, and slightly scary, writeup from Kieran Healy puts a revolutionary-spin on the current NSA PRISM debacle, showing how even all this “useless metadata” can be collected into scary and telling pieces of information. Specifically, how the membership rosters of 7 social clubs could be used to find the central traitor in the midst, Paul Revere.
He seems to bridge several groups in an unusual (though perhaps not unique) way. His name is Paul Revere.
Once again, I remind you that I know nothing of Mr Revere, or his conversations, or his habits or beliefs, his writings (if he has any) or his personal life. All I know is this bit of metadata, based on membership in some organizations. And yet my analytical engine, on the basis of absolutely the most elementary of operations in Social Networke Analysis, seems to have picked him out of our 254 names as being of unusual interest.
via Using Metadata to find Paul Revere – Kieran Healy.
Volume Visualization tool “Voreen” has just dropped version 4.3 with some impressive new features, including a new NVidia-only OpenGL4.3-based mode for polyhedral geometries.
Voreen 4.3 adds two new raycasters: An octree-based out-of-core renderer implemented in OpenCL (sample workspace: raycasting-largedata-cl.vws), and a multi-volume raycaster implemented in OpenGL 4.3 (currently only on NVIDIA) that supports polyhedral proxy-geometries as well as integration of semi-transparent geometries (sample workspace: raycasting-multivolumegeometry.vws).
via Voreen 4.3 Released | Voreen – Volume Rendering Engine (Official Project Website).
Doug McCune has an interesting way of representing 24-hour cyclical data over an infinity symbol (the lemniscate).
You follow the time by working your way around the infinity. If you start at the top of the symbol at noon, you would start moving around clockwise to 1pm, then 2pm, etc. You’ll reach the center at 6pm, at which point the symbol crosses itself and you then read it counter-clockwise around the bottom.
What you end up with is a way of dividing up the times of day into quadrants. The top-left quarter of the image is the morning, from 6am-12pm. Then the top-right is the afternoon, from 12pm-6pm. Then you have the evening in the botom left (6pm-midnight) and then late-night is in the bottom-right (12am-6am). These quarters match well with how I mentally categorize times of day.
It definitely looks neat, and has some interesting properties, but all in all seems to be a bit more confusing than beneficial. Perhaps his chosen data is simply a poor example, or perhaps expanding the symbol’s width would help to eliminate some of the clutter and confusion. Either way, it’s an interesting idea.
via Visualizing Time with the Infinity Hour Chart | Doug McCune.
You probably don’t think of farmers are very data-driven or technology conscious people, but in a recent talk from Monsanto they discussed how they’re using many big-data technologies to help the more technologically-oriented farmer of the 21st century use remote sensing and the power of the Cloud to more intelligently select crops.
The proof is how much data the company tracks on a regular basis (see Monsanto’s chart above). Monsanto’s core projects generate huge amounts of bits, especially its genomic efforts, which are the focus of so much public attention. Other big data gobblers are the phenotypes of millions of DNA structures that describe the various biological properties of each plant, and the photographic imagery of crop fields. All told, there are several tens of petabytes that need storage and analysis, a number that’s doubling roughly every 16 months. As a result, Monsanto has become a big user of Hadoop, H Base and other analytics-and-storage tools.
This could be a great opportunity for some interesting visualization research, merging the photographic and remote sensing data into statistical growth models to show predictions of output and estimate effectiveness of various crops.
via How Monsanto Is Expanding Its Footprint Through Data Analytics.
Robert Kosara reminds us that the IEEE VIS deadline for tutorials is coming up soon, and they’re still looking for reviewers. In addition, the new “Industry and Government Experience” track looks particularly interesting.
Posters should present case studies of success, failure, experimentation, design, development, methodology, best practice lessons and other topics relevant to a commercial context. Discussion could include one or more of what were the business objectives, who were the users, what were their needs, what were the design objectives, what technology was developed, what was the result, how did it perform, what were the lessons learned and what are the next steps?
Having spent several years myself in government doing visualization, I know there’s a fundamental misunderstanding in many folks about what visualization can (and more importantly should do). Many see it as simply a waste of time and money, preferring massive charts of impossibly small text and numbers, because the powerpoint slide full of 8-point font looks impressive to stakeholders, regardless of what it says. Many don’t understand that visualization can be a far more effective method of changing perception and understanding of a problem, and were really amazed at what some very basic visualization techniques can do.
So if you have any (publicly-releasable) examples of good (or bad, I suppose) uses of visualization in government or industry, considering submitting.
via VIS Tutorials and the Industry and Government Experiences Track | eagereyes.
Over at FlowingData, Nathan Yau is running a simple contest to give away a signed copy of his book “Data Points“.
It’s hard to believe Data Points hit the shelves two months ago. (Thank you to everyone who got a copy!) It still feels brand new in my head. I kind of thought that time would slow down after I finished the book (and dissertation), but it seems to be moving even faster now.
Hit his website and leave a comment for a chance to get a free copy! Or just buy it from Amazon and enjoy it now.
via Comment to win a signed copy of Data Points.
Those of you working in the Image Processing, particularly the biomedical imaging space, will be excited to hear that KitWare has just released ITK 4.4.0!
Kitware and the Insight Toolkit (ITK) team are pleased to announce ITK 4.4.0! The tarballs can be downloaded on the Sourceforge page. Major changes made in this release are detailed below, and there will be an ITK 4.4.0 release webinar on Tuesday, June 11th at 3PM ET to provide an overview of the new version. To take part, join the “ITK BarCamp” GooglePlus community, and join the “What’s New in Release 4.4.0” event.
This version has new MINC, SCIFIO, and DCMTK support, as well as the usual performance improvements and bugfixes. There is, however, a known issue in Debug mode under Visual Studio which is already fixed and available in nightlies, and will be part of the soon-to-come 4.4.1.
via Kitware – News: Announcing ITK 4.4.0!.
Robert Kosara has an interesting piece about the “banking to 45-degrees” phenomena in line-charts and its origin in a 1988 paper.
What people often miss, however, is what task Cleveland and the McGills were after. The paper is very specifically about the comparison between the slopes of two lines, and the slope is the average between those two lines. So if the goal is to be able to compare the rates of change between lines, the 45º average slope makes sense as a rule. It may be a good idea in other circumstances as well, but this particular study does not offer any information to support that.
Interestingly, at last year’s InfoVis some researchers replicated the work and found it not entirely accurate, but rather the 45-degree result was a result of boundary conditions.
via Aspect Ratio and Banking to 45 Degrees | eagereyes.
Google Maps is a huge project involving AI, computer vision, big data, and lots of physical hardware and people. At the recent Google I/O event, they discussed one project “Ground Truth” that constantly refines the data to ensure accuracy and relevance.
For example, Google might get road information from a government that indicates a particular road is a two-way street. But Google’s own traffic data, gathered anonymously from people’s Android phone data, might show that it appears to be a one-way street. A Ground Truth operator then can look at Google Street View data, including signs, to see what’s actually there, he said.
via With Ground Truth, Google marries virtual world with the real | Internet & Media – CNET News.