Standards proliferation is driven by standards organizations, their staffs, their members and others.
Topic maps can’t stop the proliferation of standards any more than endless ontology discussions will result in a single ontology.
Topic maps can provide one or more views of a mapping between standards.
Views to help you transition between standards or to produce data serialized according to different standards.
The Big Data story told through amusing pictures
Extremely funny!
My personal favorite:
Big Data Is Like Teenage Sex;
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is
doing it, so everyone claims they
are doing it….
There is far more truth in that quote than most vendors would care to admit.
Enjoy!
The Taxonomy of Terrible Programmers by Aaron Stannard.
From the post:
The MarkedUp Analytics team had some fun over the past couple of weeks sharing horror stories about software atrocities and the real-life inspirations for the things you read on The Daily WTF. In particular, we talked about bad apples who joined our development teams over the years and proceeded to ruin the things we love with poor judgment, bad habits, bad attitudes, and a whole lot of other bizarre behavior that would take industrial pyschologists thousands of years to document, let alone analyze.
So I present you with the taxonomy of terrible software developers, the ecosystem of software critters and creatures who add a whole new meaning to the concept of “defensive programming.”
At one point or another, every programmer exists as at least one of these archetypes – the good ones see these bad habits in themselves and work to fix them over time. The bad ones… simply are.
You need to see Aaron’s post for the details but I will list the categories to whet your appetite:
Enjoy!
Maps Of Seven Deadly Sins In America
From the post:
Geographers from Kansas State University have created a map of the spatial distribution of the Seven Deadly Sins across the United States. How? By mapping demographic data related to each of the Sins.
Below are screenshots of the maps in standard deviation units; red naturally is more sinful, blue less sinful.
I’m not vouching for the accuracy of these maps. 😉
I could not find the original project, which was apparently a presentation at a geography conference in Las Vegas.
Has anyone mapped the levels in Dante’s Inferno to a U.S. map?
Based on crime and other socio-economic data?
That would be a real interesting map.
Although not the sort of thing you would find at the tourist bureau.
SCIgen – An Automatic CS Paper Generator by Jeremy Stribling, Max Krohn, and Dan Aguayo.
From the webpage:
SCIgen is a program that generates random Computer Science research papers, including graphs, figures, and citations. It uses a hand-written context-free grammar to form all elements of the papers. Our aim here is to maximize amusement, rather than coherence.
One useful purpose for such a program is to auto-generate submissions to conferences that you suspect might have very low submission standards. A prime example, which you may recognize from spam in your inbox, is SCI/IIIS and its dozens of co-located conferences (check out the very broad conference description on the WMSCI 2005 website). There’s also a list of known bogus conferences. Using SCIgen to generate submissions for conferences like this gives us pleasure to no end. In fact, one of our papers was accepted to SCI 2005! See Examples for more details.
We went to WMSCI 2005. Check out the talks and video. You can find more details in our blog.
…
WARNING: As per the honor code promise to do your own work, write your own CS paper generator for class submissions.
Curious if anyone has extended this code to allow for customization for specific subject areas?
Or updated the vocabulary?
BTW, the world record for fraudulent papers is held by Yoshitaka Fujii, at 172. (The Problem with Peer Review by Natalie Healey.)
How long would it take to break that record in conference versus journal submissions? Which one would top 172 first? Conferences or journals?
Bourbon family tree by Nathan Yau.
Nathan has located and reproduced a family tree for bourbon produced by the major distillers in three states.
Print out and use this over the holidays to track your drinking across family lines. 😉
16 Reasons Data Scientists are Difficult to Manage
No spoilers. Go read Amy’s post.
I think it would have worked better as:
Data Scientist Scoring Test.
With values associated with the answers.
You?
Data Mining Book Review: How to Lie with Statistics by Sandro Saitta.
Sandro reviews “How to Lie with Statistics.”
It’s not a “recent” publication. 😉
However, it is an extremely amusing publication.
If you search for “How to Lie with Statistics PDF” I am fairly sure you will turn up copies on the WWW.
Enjoy!
Greg Linden pointed out an excellent xkcd cartoon composed of auto-completed questions from Google.
Maximize your enjoyment by entering a few of the terms in your search box.
The auto-completed questions and their “answers” may surprise you.
Most popular porn searches, by state by Nathan Yau.
I can’t say I am surprised by hentai being the most popular search in Alabama.
Looking forward to seeing a color version at state hospitality centers. 😉
Just too bizarre to pass on re-posting.
I found this and other material suitable for training students what not to do, at: WTF Visualizations.
An ignored issue in Big Data analysis by Kaiser Fung.
Kaiser debunks a couple of recent stories that were powered, so it was said, by “analysis” of “big data.”
Short, highly amusing and worth your time to read.
If you practice this type of statistical analysis (or lack thereof) you need to also be using Bible codes. Or a Ouija Board.
I was reading the source document in: DHS Bridging Siloed Databases [Comments?] when I encountered a possible reason for the Snowden security breach.
Records in this system are stored electronically in secure facilities in a locked drawer behind a locked door. The records may be stored on magnetic disc, tape, or digital media.
You might want to read that again:
Records in this system are stored electronically in secure facilities in a locked drawer behind a locked door. The records may be stored on magnetic disc, tape, or digital media.
Something about storing records electronically “…in a locked drawer behind a locked door” tips me off to the writer not having a clear idea about computer security.
Here is one document that has this language:
Which covered:
CATEGORIES OF RECORDS IN THE SYSTEM: (1) Motor Vehicle Accident Reports. (2) Parking Permits. (3) Distribution lists of individuals requesting various Treasury publications. (4) Treasury Credentials.
And it reads:
Records in this system are stored electronically or on paper in secure facilities in a locked drawer behind a locked door. (emphasis added)
For paper records, ok. For electronic records, not so hot.
I’m not real sure what “a locked drawer behind a locked door” would mean for electronic records. Assuming anyone wanted to use or search the records. Maybe you could put them on a thumb-drive. ;-)`
Update: One of my regulars correspondents will accuse me of being obscure: Why Computer Security Fails? Ignorance. It’s just that simple.
Samatha Bail has discovered a missing layer in the Semantic Web Stack!
In topic maps we call that semantic diversity. 😉
Animated graphic with an important lesson about data security.
Snooping isn’t limited to being convenient or even electronic.
Extensive timelines of slang for genitalia by Nathan Yau.
Nathan has discovered interactive time lines of slang for male and female genitalia. (Goes back to 1250-1300 CE.)
If you know Anthony Weiner, please forward these links to his attention.
If you don’t know Anthony Weiner, take this as an opportunity to expand your twitter repartee.
I saw a tweet today by nickbarnwell:
Pro-tip: “Hickey Facts” and “Hickey Facts Datomic” turn up vastly different search results #clojure #datomic
Don’t take my word for it:
In case you don’t already know the answer, the first query returns “about” 1,400,000 results and the second query “about” 12,000 results. 😉
Dashboard Requirement Gathering Satire by Nick Barclay.
From the post:
A colleague of mine put together a hilarious PeepzMovie that was inspired by some frustrating projects we’re working on currently.
If you’re a BI pro, do yourself a favor and take a few mins to watch it.
Watch the video at Nick’s post.
Show of hands: Who has not had this experience when developing requirements?
😉
What is wrong with these charts? by Nathan Yau.
If you pride yourself on spotting mistakes, Nathan Yau has a chart for you!
Pair up and visit Nathan’s post. See if you and a friend spot the same errors.
Suggest you repeat the exercise with your next presentation but tell the contestants the slides are from someone else. 😉
Joel Grus posted an updated version of Drew Conway’s Data Science Venn Diagram.
Pornography: what we know, what we don’t by Mona Chalabi.
From the post:
Unsurprisingly, on the Datablog we often write articles about data when we have data. But some topics, like pornography, aren’t conducive to statistical analysis, no matter how important many claim they are.
Despite these challenges, a report released today has sought to assess children and young people’s exposure to pornography and understand its impact. Led by Middlesex University and commissioned by the Children’s Commissioner, this was a rapid evidence assessment – completed in the space of just three months as part of a much larger ongoing inquiry into child sexual exploitation.
The report found that a “significant proportion of children and young people are exposed to or access pornography”, and that this is linked to “unrealistic attitudes about sex” as well as “less progressive gender role attitudes (e.g. male dominance and female submission)”.
Though the report makes these and other important conclusions, you’ll notice that numbers are conspicuously absent in its language. One reason is that its findings were not based on primary research but a literature review that began with 41,000 identified sources and concluded by using 276 of those that were deemed relevant.
The post doesn’t even mention that we will know pornography when we see it.
😉
Perhaps that is part of the problem of measurement.
Rather than processing a trillion triples, the next big data measure should be indexing all the pornography on the WWW over some time period.
Yes?
NSA — Untangling the Web: A Guide to Internet Research
A Freedom of Information Act (FOIA) request caused the NSA to disgorge its guide to web research, which is some six years out of date.
From the post:
The National Security Agency just released “Untangling the Web,” an unclassified how-to guide to Internet search. It’s a sprawling document, clocking in at over 650 pages, and is the product of many years of research and updating by a NSA information specialist whose name is redacted on the official release, but who is identified as Robyn Winder of the Center for Digital Content on the Freedom of Information Act request that led to its release.
It’s a droll document on many levels. First and foremost, it’s funny to think of officials who control some of the most sophisticated supercomputers and satellites ever invented turning to a .pdf file for tricks on how to track down domain name system information on an enemy website. But “Untangling the Web” isn’t for code-breakers or wire-tappers. The target audience seems to be staffers looking for basic factual information, like the preferred spelling of Kazakhstan, or telephonic prefix information for East Timor.
I take it as guidance on how “good” does your application or service need to be to pitch to the government?
I keep thinking to attract government attention, an application needs to fall just short of solving P = NP?
On the contrary, the government needs spell checkers, phone information and no doubt lots of other dull information, quickly.
Perhaps an app that signals fresh doughnuts from bakeries within X blocks would be just the thing. 😉
Beer Mapper: An experimental app to find the right beer for you by Nathan Yau.
Nathan reviews an app that with a data set of 10,000 beers, attempts to suggest similar beers based on your scoring of beers.
A clever app but I am betting on Lars Marius besting it more often than not!
What is The ROI of Ignorance? by Timo Elliott.
Some quants will be disappointed but it’s a fair estimate:
The topic map committee choose to not provide guidance on creating topic maps.
In hindsight, I think that was a mistake. A big one.
How else can users know to avoid CNN.com when creating serious topic maps?
Instead of ISO/IEC SC34/WG3, people have to rely on Jon Steward to get that information:
Jon Stewart Rips Into CNN For Lying About The Boston Marathon
I like Jon Stewart but an SDO he’s not. I doubt he is even a member of a national body.
I can imagine using CNN for a topic map, one about sexual graffiti in the catacombs of Rome being investigated by Geraldo Rivera.
And for a topic map on the descent of journalism into 24×7 infotainment.
But outside of that…, not a chance!
Big Data Defined by Russell Jurney.
From the post:
Specifically, a Big Data system has four properties:
- It uses local storage to be fast but inexpensive
- It uses clusters of commodity hardware to be inexpensive
- It uses free software to be inexpensive
- It is open source to avoid expensive vendor lock-in
It has been raining all day but I had to laugh when I saw Russell’s definition of “a Big Data system.”
Does it remind you of any particular player in the Big Data pack? 😉
That’s one way to build marketshare, you define yourself to be the measuring stick.
Let’s walk through the list and see what comments or alternatives suggest themselves:
[What? No cloud? Have you compared all the cost of local hardware against the cloud?]
[Wonder why NCSA build Blue Waters “from Cray hardware, operates at a sustained performance of more than 1 petaflop (1 quadrillion calculations per second) and is capable of peak performance of 11.61 petaflops (11.6 quadrillion calculations per second).” Must not be “big data.]
[They say that so often. I wonder what they are using as a basis for comparison? LaTeX versus MS Word? Have you paid anyone to typeset a paper in LaTeX versus asking your staff to type it in MS Word?]
[Actually it is open formats that avoid vendor lock-in, expensive or otherwise]
I enjoy a bit of marketing fluff as much as the next person but it should at least be plausible.
Interpreting scientific literature: A primer by kshameer.
It’s visual so follow the link.
I shouldn’t re-post this sort of thing, being something of a professional academic, but it’s too funny to resist.
Would be interesting to create an auto-tagger that could be run against online text to supply markup with the “they mean” values to be displayed on command.
😉
I first saw this at Christophe Lalanne’s A bag of tweets / January 2013.
Searching for “Biscuit Bliss,” a book of biscuit recipes, also had the result:
People also search for
The Glory of Southern Cooking James Villas
The Bacon Cookbook James Villas
Texas home cooking Cheryl Jamison
The Joy of Pregnancy
Pie Ken Haedrich
If I were writing associations for “Biscuit Bliss,” pie would not make the list.
Bacon I can see because it is a major food group along side biscuits.
I suppose the general cooking books are super-classes of biscuit making.
Some female friends have suggested eating is associated with pregnancy.
True, but when I search for “joy of pregnancy,” it doesn’t suggest cookbooks in general or biscuits in particular.
If there is an association, is it non-commutative?*
Suggested associations of biscuits with pregnancy? (mindful of the commutative/non-commutative question)
* I am not altogether certain what a non-commutative association would look like. Partial ignorance from a point of view?
One player in the association having knowledge of the relationship and the other player does not?
Some search engines already produce that result, whether by design or not I don’t know.
When Oracle bought MySQL from DBA Reactions.
Start your topic map day’s reading with some humor!
More seriously, suggestions of pics or video clips with topic map related captions welcome! (for the renovated topicmaps.com).
If we can’t smile at ourselves, very few are going to smile on us.
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