Data Science Challenge 3

October 24th, 2014

Data Science Challenge 3

From the post:

Challenge Period

The Fall 2014 Data Science Challenge runs October 11, 2014 through January 21, 2015.

Challenge Prerequisite

You must pass Data Science Essentials (DS-200) prior to registering for the Challenge.

Challenge Description

The Fall 2014 Data Science Challenge incorporates three independent problems derived from real-world scenarios and data sets. Each problem has its own data, can be solved independently, and should take you no longer than eight hours to complete. The Fall 2014 Challenge includes problems dealing with online travel services, digital advertising, and social networks.

Problem 1: SmartFly
You have been contacted by a new online travel service called SmartFly. SmartFly provides its customers with timely travel information and notifications about flights, hotels, destination weather, and airport traffic, with the goal of making your travel experience smoother. SmartFly’s product team has come up with the idea of using the flight data that it has been collecting to predict whether customers’ flights will be delayed in order to respond proactively. The team has now contacted you to help test out the viability of the idea. You will be given SmartFly’s data set from January 1 to September 30, 2014 and be asked to return a list of of upcoming flights sorted from the most likely to the least likely to be delayed.

Problem 2: Almost Famous
Congratulations! You have just published your first book on data science, advanced analytics, and predictive modeling. You’ve also decided to use your skills as a data scientist to build and optimize a website that promotes your book, and you have started several ad campaigns on a popular search engine in order to drive traffic to your site. Using your skills in data munging and statistical analysis, you will be asked to evaluate the performance of a series of campaigns directed towards site visitors using the log data in Hadoop as your source of truth.

Problem 3: WINKLR
WINKLR is a curiously popular social network for fans of the 1970s sitcom Happy Days. Users can post photos, write messages, and, most importantly, follow each other’s posts. This helps members keep up with new content from their favorite users. To help its users discover new people to follow on the site, WINKLR is building a new machine learning system called The Fonz to predict who a given user might like to follow. Phase One of The Fonz project is underway. The engineers can export the entire user graph as tuples. You have joined the Fonz project to implement Phase Two, which improves on this result. Given the user graph and the list of frequent-click tuples, you are being asked to select a 70,000 tuple subset in “user1,user2″ format, where you believe user1 is mostly likely to want to follow user2. These will result in emails to the users, inviting them to follow the recommended user.

Prize for success: CCP: Data Scientist status

Great way to start 2015!

I first saw this in a tweet by Sarah.

The Pretence of Knowledge

October 24th, 2014

The Pretence of Knowledge by Friedrich August von Hayek. (Nobel Prize Lecture in Economics, December 11, 1974)

From the lecture:

The particular occasion of this lecture, combined with the chief practical problem which economists have to face today, have made the choice of its topic almost inevitable. On the one hand the still recent establishment of the Nobel Memorial Prize in Economic Science marks a significant step in the process by which, in the opinion of the general public, economics has been conceded some of the dignity and prestige of the physical sciences. On the other hand, the economists are at this moment called upon to say how to extricate the free world from the serious threat of accelerating inflation which, it must be admitted, has been brought about by policies which the majority of economists recommended and even urged governments to pursue. We have indeed at the moment little cause for pride: as a profession we have made a mess of things.

It seems to me that this failure of the economists to guide policy more successfully is closely connected with their propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences – an attempt which in our field may lead to outright error. It is an approach which has come to be described as the “scientistic” attitude – an attitude which, as I defined it some thirty years ago, “is decidedly unscientific in the true sense of the word, since it involves a mechanical and uncritical application of habits of thought to fields different from those in which they have been formed.”1 I want today to begin by explaining how some of the gravest errors of recent economic policy are a direct consequence of this scientistic error.

If you have some time for serious thinking over the weekend, visit or re-visit this lecture.

Substitute “computistic” for “scientistic” and capturing semantics as the goal.

Google and other search engines are overwhelming proof that some semantics can be captured by computers, but they are equally evidence of a semantic capture gap.

Any number of proposals exist to capture semantics, ontologies, Description Logic, RDF, OWL, but none are based on an empirical study how semantics originate, change and function in human society. Such proposals are snapshots of a small group’s understanding of semantics. Your mileage may vary.

Depending on your goals and circumstances, one or more proposal may be useful. But capturing and maintaining semantics without a basis in empirical study of semantics seems like a hit or miss proposition.

Or at least historical experience with capturing and maintaining semantics points in that direction.

I first saw this in a tweet by Chris Diehl

15 Tricks to Appear Smart in Emails

October 24th, 2014

15 Tricks to Appear Smart in Emails by Sarah Cooper.

From the post:

If you don’t care about appearing smart in emails, you can stop reading now.

Oh good, we’re alone.

In the corporate world, there is no ground more fertile for appearing smart than the rich earth that is electronic communication. Your email writing, sending and ignoring skills are just as important as your nodding skills, and even more important than your copying and pasting skills. Here are 15 email tricks that will make you appear smart, passionate, dedicated and most of all, smart.

Great illustrations to go along with the 15 tricks so see Sarah’s post.


Update: Is this beat up on email day? See: University administrator demands new email emphasis tool

Edinburgh. A University administrator has demanded a new tool with which to emphasize parts of e-mails, having exhausted traditional methods such as bold, italics, red text and flashing text.

“The simple fact is that people ignore my emails” said Ima Jobsworth, a senior administrator at the University of Berwick. “In the early days I used bold and italics to emphasize parts of the text, and people paid attention” he contnued. “But then they figured out that the bold and italicised sections were just as irrelevant to them as the rest of the email, perhaps even more so”.

Analysis of Named Entity Recognition and Linking for Tweets

October 24th, 2014

Analysis of Named Entity Recognition and Linking for Tweets by Leon Derczynski, et al.

Abstract:

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identi cation, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

A detailed review of existing solutions for mining tweets, where they fail along and why.

A comparison to spur tweet research:

Tweets Per Day > 500,000,000 Derczynski, p. 2
Annotated Tweets < 10,000 Derczynski, p. 27

Let’s see: 500,000,000 / 10,000 = 50,000.

The number of tweet per day is more than 50,000 times the number of tweets annotated with named entity types.

It may just be me but that sounds like the sort of statement you would see in a grant proposal to increase the number of annotated tweets.

Yes?

I first saw this in a tweet by Diana Maynard.

50 Face Recognition APIs

October 24th, 2014

50 Face Recognition APIs by Mirko Krivanek.

Interesting listing published on Mashape. Only the top 12 are listed below. It would be nice to have a separate blog for voice recognition APIs. I’ve been thinking at using voice rather than passport or driving license, as a more secure ID. The voice has a texture unique to each individual.

Subjects that are likely to be of interest!

Mirko mentions voice but then lists face recognition APIs.

Voice comes up in a mixture of APIs in: 37 Recognition APIS: AT&T SPEECH, Moodstocks and Rekognition by Matthew Scott.

I first saw this in a tweet by Andrea Mostosi

analyze survey data for free

October 24th, 2014

Anthony Damico has “unlocked” a number of public survey data sets with blog posts that detail how to analyze those sets with R.

Forty-six (46) data set are covered so far:

unlocked public-use data sets

An impressive donation of value to R and public data and an example that merits emulation! Pass this along.

I first saw this in a tweet by Sharon Machlis.

analyze the public libraries survey (pls) with r

October 24th, 2014

analyze the public libraries survey (pls) with r by Anthony Damico.

From the post:

each and every year, the institute of museum and library services coaxes librarians around the country to put down their handheld “shhhh…” sign and fill out a detailed online questionnaire about their central library, branch, even bookmobile. the public libraries survey (pls) is actually a census: nearly every public library in the nation responds annually. that microdata is waiting for you to check it out, no membership required. the american library association estimates well over one hundred thousand libraries in the country, but less than twenty thousand outlets are within the sample universe of this survey since most libraries in the nation are enveloped by some sort of school system. a census of only the libraries that are open to the general public, the pls typically hits response rates of 98% from the 50 states and dc. check that out.

A great way to practice your R skills!

Not to mention generating analysis to support your local library.

Strong Passwords – Myths of CS?

October 24th, 2014

Do we really need strong passwords? by Mark Stockley.

Mark reviews “An Administrator’s Guide to Internet Password Research” by Dinei Florêncio, Cormac Herley and Paul C. van Oorschot.

From the post:

The authors, Dinei Florêncio, Cormac Herley and Paul C. van Oorschot, contend that “much of the available guidance lacks supporting evidence” and so set out to examine the usefulness of (among other things) password composition policies, forced password expiration and password lockouts.

They also set out to determine just how strong a password used on a website needs to be to withstand a real-world attack.

Their conclusion is that creating strong passwords is wasted effort a lot of the time.

They suggest that organisations should invest their own resources in securing systems rather than simply offloading the cost to end users in the form of advice, demands or enforcement policies that are often pointless.

To understand their conclusions we need to look at the difference between online and offline attacks.

Don’t take the conclusion:

that creating strong passwords is wasted effort a lot of the time.

You need to read Mark’s post in full and/or the article to know when it is “a lot of the time.”

The abstract from the article:

The research literature on passwords is rich but little of it directly aids those charged with securing web-facing services or setting policies. With a view to improving this situation we examine questions of implementation choices, policy and administration using a combination of literature survey and first-principles reasoning to identify what works, what does not work, and what remains unknown. Some of our results are surprising. We find that offline attacks, the justification for great demands of user effort, occur in much more limited circumstances than is generally believed (and in only a minority of recently-reported breaches). We find that an enormous gap exists between the effort needed to withstand online and offline attacks, with probable safety occurring when a password can survive 106 and 1014 guesses respectively. In this gap, eight orders of magnitude wide, there is little return on user effort: exceeding the online threshold but falling short of the offline one represents wasted effort. We find that guessing resistance above the online threshold is also wasted at sites that store passwords in plaintext or reversibly encrypted: there is no attack scenario where the extra effort protects the account.

Empirical research is creating a new genre of mythology. Computer Science Mythology, coming to a bookstore near you.

Analyzing Schema.org

October 23rd, 2014

Analyzing Schema.org by Peter F. Patel-Schneider.

Abstract:

Schema.org is a way to add machine-understandable information to web pages that is processed by the major search engines to improve search performance. The definition of schema.org is provided as a set of web pages plus a partial mapping into RDF triples with unusual properties, and is incomplete in a number of places. This analysis of and formal semantics for schema.org provides a complete basis for a plausible version of what schema.org should be.

Peter’s analysis is summarized when he says:

The lack of a complete definition of schema.org limits the possibility of extracting the correct information from web pages that have schema.org markup.

Ah, yes, “…the correct information from web pages….”

I suspect the lack of semantic precision has powered the success of schema.org. Each user of schema.org markup has their private notion of the meaning of their use of the markup and there is no formal definition to disabuse them of that notion. Not that formal definitions were enough to save owl:sameAs from varying interpretations.

Schema.org empowers varying interpretations without requiring users to ignore OWL or description logic.

For the domains that schema.org covers, eateries, movies, bars, whore houses, etc., the semantic slippage permitted by schema.org lowers the bar to usage of its markup. Which has resulted in its adoption more widely than other proposals.

The lesson of schema.org is the degree of semantic slippage you can tolerate depends upon your domain. For pharmaceuticals, I would assume that degree of slippage is as close to zero as possible. For movie reviews, not so much.

Any effort to impose the same degree of semantic slippage across all domains is doomed to failure.

I first saw this in a tweet by Bob DuCharme.

Rich Citations: Open Data about the Network of Research

October 23rd, 2014

Rich Citations: Open Data about the Network of Research by Adam Becker.

From the post:

Why are citations just binary links? There’s a huge difference between the article you cite once in the introduction alongside 15 others, and the data set that you cite eight times in the methods and results sections, and once more in the conclusions for good measure. Yet both appear in the list of references with a single chunk of undifferentiated plain text, and they’re indistinguishable in citation databases — databases that are nearly all behind paywalls. So literature searches are needlessly difficult, and maps of that literature are incomplete.

To address this problem, we need a better form of academic reference. We need citations that carry detailed information about the citing paper, the cited object, and the relationship between the two. And these citations need to be in a format that both humans and computers can read, available under an open license for anyone to use.

This is exactly what we’ve done here at PLOS. We’ve developed an enriched format for citations, called, appropriately enough, rich citations. Rich citations carry a host of information about the citing and cited entities (A and B, respectively), including:

  • Bibliographic information about A and B, including the full list of authors, titles, dates of publication, journal and publisher information, and unique identifiers (e.g. DOIs) for both;
  • The sections and locations in A where a citation to B appears;
  • The license under which B appears;
  • The CrossMark status of B (updated, retracted, etc);
  • How many times B is cited within A, and the context in which it is cited;
  • Whether A and B share any authors (self-citation);
  • Any additional works cited by A at the same location as B (i.e. citation groupings);
  • The data types of A and B (e.g. journal article, book, code, etc.).

As a demonstration of the power of this new citation format, we’ve built a new overlay for PLOS papers, which displays much more information about the references in our papers, and also makes it easier to navigate and search through them. Try it yourself here: http://alpha.richcitations.org.
The suite of open-source tools we’ve built make it easy to extract and display rich citations for any PLOS paper. The rich citation API is available now for interested developers at http://api.richcitations.org.

If you look at one of the test articles such as: Jealousy in Dogs, the potential of rich citations becomes immediately obvious.

Perhaps I was reading “… the relationship between the two…” a bit too much like an association between two topics. It’s great to know how many times a particular cite occurs in a paper, when it is a self-citation, etc. but is a long way from attaching properties to an association between two papers.

On the up side, however, PLOS is already has 10,000 papers with “smart cites” with more on the way.

A project to watch!

Avoiding “Hive” Confusion

October 23rd, 2014

Depending on your community, when you hear “Hive,” you think “Apache Hive:”

The Apache Hive ™ data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.

But, there is another “Hive,” which handles large datasets:

High-performance Integrated Virtual Environment (HIVE) is a specialized platform being developed/implemented by Dr. Simonyan’s group at FDA and Dr. Mazumder’s group at GWU where the storage library and computational powerhouse are linked seamlessly. This environment provides web access for authorized users to deposit, retrieve, annotate and compute on HTS data and analyze the outcomes using web-interface visual environments appropriately built in collaboration with research scientists and regulatory personnel.

I ran across this potential source of confusion earlier today and haven’t run it completely to ground but wanted to share some of what I have found so far.

Inside the HIVE, the FDA’s Multi-Omics Compute Architecture by Aaron Krol.

From the post:

“HIVE is not just a conventional virtual cloud environment,” says Simonyan. “It’s a different system that virtualizes the services.” Most cloud systems store data on multiple servers or compute units until users want to run a specific application. At that point, the relevant data is moved to a server that acts as a node for that computation. By contrast, HIVE recognizes which storage nodes contain data selected for analysis, then transfers executable code to those nodes, a relatively small task that allows computation to be performed wherever the data is stored. “We make the computations on exactly the machines where the data is,” says Simonyan. “So we’re not moving the data to the computational unit, we are moving computation to the data.”

When working with very large packets of data, cloud computing environments can sometimes spend more time on data transfer than on running code, making this “virtualized services” model much more efficient. To function, however, it relies on granular and readily-accessed metadata, so that searching for and collecting together relevant data doesn’t consume large quantities of compute time.

HIVE’s solution is the honeycomb data model, which stores raw NGS data and metadata together on the same network. The metadata — information like the sample, experiment, and run conditions that produced a set of NGS reads — is stored in its own tables that can be extended with as many values as users need to record. “The honeycomb data model allows you to put the entire database schema, regardless of how complex it is, into a single table,” says Simonyan. The metadata can then be searched through an object-oriented API that treats all data, regardless of type, the same way when executing search queries. The aim of the honeycomb model is to make it easy for users to add new data types and metadata fields, without compromising search and retrieval.

Popular consumption piece so next you may want to visit the HIVE site proper.

From the webpage:

HIVE is a cloud-based environment optimized for the storage and analysis of extra-large data, like Next Generation Sequencing data, Mass Spectroscopy files, Confocal Microscopy Images and others.

HIVE uses a variety of advanced scientific and computational visualization graphics, to get the MOST from your HIVE experience you must use a supported browser. These include Internet Explore 8.0 or higher (Internet Explorer 9.0 is recommended), Google Chrome, Mozilla Firefox and Safari.

A few exemplary analytical outputs are displayed below for your enjoyment. But before you can take advantage of all that HIVE has to offer and create these objects for yourself, you’ll need to register.

With A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE) by Tsung-Jung Wu, et al., you are starting to approach the computational issues of interest for data integration.

From the article:

The forementioned cooperation is difficult because genomics data are large, varied, heterogeneous and widely distributed. Extracting and converting these data into relevant information and comparing results across studies have become an impediment for personalized genomics (11). Additionally, because of the various computational bottlenecks associated with the size and complexity of NGS data, there is an urgent need in the industry for methods to store, analyze, compute and curate genomics data. There is also a need to integrate analysis results from large projects and individual publications with small-scale studies, so that one can compare and contrast results from various studies to evaluate claims about biomarkers.

See also: High-performance Integrated Virtual Environment (Wikipedia) for more leads to the literature.

Heterogeneous data is still at large and people are building solutions. Rather than either/or, what do you think topic maps could bring as a value-add to this project?

I first saw this in a tweet by ChemConnector.

Results of 2014 State of Clojure and ClojureScript Survey

October 23rd, 2014

Results of 2014 State of Clojure and ClojureScript Survey by Alex Miller.

From the post:

The 2014 State of Clojure and ClojureScript Survey was open from Oct. 8-17th. The State of Clojure survey (which was applicable to all users of Clojure, ClojureScript, and ClojureCLR) had 1339 respondents. The more targeted State of ClojureScript survey had 642 respondents.

The responses to “What has been most frustrating for you in your use of Clojure/CLJS? put “Availability of comprehensive / approachable documentation, tutorials, etc” at #2 and #3 respectively.

Improved technical capabilities is important for existing users but increasing mind share is an issue of “onboarding” new users of Clojure. If you have ever experienced or “read about” the welcoming given even casual visitors in some churches, you will have a good idea of some effective ideas at building membership.

If you try to build a community using techniques not found in churches, you need better techniques. Remember churches have had centuries to practice their membership building techniques.

Let me put it this way: When was the last time you saw a church passing out information as poorly written, organized and incomplete as that for most computer languages? Guess who is winning the membership race by any measure?

Are you up for studying and emulating building church membership techniques? (as appropriate or adapted)

R Programming for Beginners

October 23rd, 2014

R Programming for Beginners by LearnR.

Short videos on R programming, running from a low of two (2) minutes (the intro) up to eight minutes (the debugging session) but generally three (3) to five (5) minutes in length. I have cleaned up the YouTube listing to make it suitable for sharing and/or incorporation into other R resources.

Enjoy!

Balisage 2015!

October 23rd, 2014

Early date and location news for Balisage 2015:

We have a date and location for Balisage 2015:

Pre-conference symposium: August 10, 2014
Balisage Conference: August 11 – 14, 2014

Same location as Balisage 2014:

Bethesda North Marriott Hotel & Conference Center,
5701 Marinelli Road, Rockville, MD, 20852-2785

(We have moved to the second week of August so we could have the same auditorium for the conference sessions and better space for posters, breaks, and such.)

Mark your calendars. Start thinking about what you want to talk about at Balisage 2015. Plan your trip to the Washington DC area.

Put plane tickets to Balisage on your holiday wish list!

Start planning your paper and slides now. Imagine what a difference ten (10) months versus the wee morning hours before will make on your slides. (No names, Eliot.)

Whatever your political leanings, the mid-term elections hold no fear. Balisage will occur in August of 2015 so all is right with the world.

Loading CSV files into Neo4j

October 22nd, 2014

Loading CSV files into Neo4j is so easy that it has taken only three (3) posts, so far, to explain the process. This post is a collection of loading CSV into Neo4j references. If you have others, feel free to contribute them and I will add them to this post.

LOAD CSV into Neo4j quickly and successfully by Michael Hunger on Jun 25, 2014.

Note: You can also read an interactive and live version of this blog post as a Neo4j GraphGist.

Since version 2.1 Neo4j provides out-of-the box support for CSV ingestion. The LOAD CSV command that was added to the Cypher Query language is a versatile and powerful ETL tool.

It allows you to ingest CSV data from any URL into a friendly parameter stream for your simple or complex graph update operation, that … conversion.

The June 25, 2014 post has content that is not repeated in the Oct. 18, 2014 post on loading CSV so you will need both posts, or a very fine memory.

Flexible Neo4j Batch Import with Groovy by Michael Hunger on Oct 9, 2014.

You might have data as CSV files to create nodes and relationships from in your Neo4j Graph Database.

It might be a lot of data, like many tens of million lines.

Too much for LOAD CSV to handle transactionally.

Usually you can just fire up my batch-importer and prepare node and relationship files that adhere to its input format requirements.

What follows is advice on when you may want to deviate from the batch-importer defaults and how to do so.

LOAD CVS with SUCCESS by Michael Hunger on Oct 18, 2014.

I have to admit that using our LOAD CSV facility is trickier than you and I would expect.

Several people ran into issues that they could not solve on their own.

My first blog post on LOAD CSV is still valid in it own right, and contains important aspects that I won’t repeat here.

Incomplete so reference LOAD CSV into Neo4j quickly and successfully while reading this post.

Others?

Consensus Filters

October 22nd, 2014

Consensus Filters by Yao Yujian.

From the post:

Suppose you have a huge number of robots/vehicles and you want all of them to track some global value, maybe the average of the weight of the fuel that each contains.

One way to do this is to have a master server that takes in everyone’s input and generates the output. So others can get it from the master. But this approach results in a single point of failure and a huge traffic to one server.

The other way is to let all robots talk to each other, so each robot will have information from others, which can then be used to compute the sum. Obviously this will incur a huge communication overhead. Especially if we need to generate the value frequently.

If we can tolerate approximate results, we have a third approach: consensus filters.

There are two advantages to consensus filters:

  1. Low communication overhead
  2. Approximate values can be used even without a consensus

Approximate results won’t be acceptable for all applications but where they are, consensus filters may be on your agenda.

Web Apps in the Cloud: Even Astronomers Can Write Them!

October 22nd, 2014

Web Apps in the Cloud: Even Astronomers Can Write Them!

From the post:

Philip Cowperthwaite and Peter K. G. Williams work in time-domain astronomy at Harvard. Philip is a graduate student working on the detection of electromagnetic counterparts to gravitational wave events, and Peter studies magnetic activity in low-mass stars, brown dwarfs, and planets.

Astronomers that study GRBs are well-known for racing to follow up bursts immediately after they occur — thanks to services like the Gamma-ray Coordinates Network (GCN), you can receive an email with an event position less than 30 seconds after it hits a satellite like Swift. It’s pretty cool that we professionals can get real-time notification of stars exploding across the universe, but it also seems like a great opportunity to convey some of the excitement of cutting-edge science to the broader public. To that end, we decided to try to expand the reach of GCN alerts by bringing them on to social media. Join us for a surprisingly short and painless tale about the development of YOITSAGRB, a tiny piece of Python code on the Google App Engine that distributes GCN alerts through the social media app Yo.

If you’re not familiar with Yo, there’s not much to know. Yo was conceived as a minimalist social media experience: users can register a unique username and send each other a message consisting of “Yo,” and only “Yo.” You can think of it as being like Twitter, but instead of 140 characters, you have zero. (They’ve since added more features such as including links with your “Yo,” but we’re Yo purists so we’ll just be using the base functionality.) A nice consequence of this design is that the Yo API is incredibly straightforward, which is convenient for a “my first web app” kind of project.

While “Yo” has been expanded to include more content, the origin remains an illustration of the many meanings that can be signaled by the same term. In this case, the detection of a gamma-ray burst in the known universe.

Or “Yo” could mean it is time to start some other activity when received from a particular sender. Or even be a message composed entirely of “Yo’s” where different senders had some significance. Or “Yo’s” sent at particular times to compose a message. Or “Yo’s” sent to leave the impression that messages were being sent. ;-)

So, does a “Yo” have any semantics separate and apart from that read into it by a “Yo” recipient?

Data Integrity and Problems of Scope

October 22nd, 2014

Data Integrity and Problems of Scope by Peter Baillis.

From the post:

Mutable state in distributed systems can cause all sorts of headaches, including data loss, corruption, and unavailability. Fortunately, there are a range of techniques—including exploiting commutativity and immutability—that can help reduce the incidence of these events without requiring much overhead. However, these techniques are only useful when applied correctly. When applied incorrectly, applications are still subject to data loss and corruption. In my experience, (the unfortunately common) incorrect application of these techniques is often due to problems of scope. What do I mean by scope? Let’s look at two examples:

Having the right ideas is not enough, you must implement them correctly as well.

Peter’s examples will sharpen your thinking about data integrity.

Enjoy!

Gram­mat­i­cal the­o­ry: From trans­for­ma­tion­al gram­mar to con­straint-​based ap­proach­es

October 22nd, 2014

Gram­mat­i­cal the­o­ry: From trans­for­ma­tion­al gram­mar to con­straint-​based ap­proach­es by Ste­fan Müller.

From the webpage:

To ap­pear 2015 in Lec­ture Notes in Lan­guage Scineces, No 1, Berlin: Lan­guage Sci­ence Press. The book is a trans­la­tion and ex­ten­sion of the sec­ond edi­tion of my gram­mar the­o­ry book that ap­peared 2010 in the Stauf­fen­burg Ver­lag.

This book in­tro­duces for­mal gram­mar the­o­ries that play a role in cur­rent lin­guis­tics or con­tribut­ed tools that are rel­e­vant for cur­rent lin­guis­tic the­o­riz­ing (Phrase Struc­ture Gram­mar, Trans­for­ma­tion­al Gram­mar/Gov­ern­ment & Bind­ing, Gen­er­al­ized Phrase Struc­ture Gram­mar, Lex­i­cal Func­tion­al Gram­mar, Cat­e­go­ri­al Gram­mar, Head-​Driv­en Phrase Struc­ture Gram­mar, Con­struc­tion Gram­mar, Tree Ad­join­ing Gram­mar). The key as­sump­tions are ex­plained and it is shown how the re­spec­tive the­o­ry treats ar­gu­ments and ad­juncts, the ac­tive/pas­sive al­ter­na­tion, local re­order­ings, verb place­ment, and fronting of con­stituents over long dis­tances. The anal­y­ses are ex­plained with Ger­man as the ob­ject lan­guage.

In a final chap­ter the ap­proach­es are com­pared with re­spect to their pre­dic­tions re­gard­ing lan­guage ac­qui­si­tion and psy­cholin­guis­tic plau­si­bil­i­ty. The Na­tivism hy­poth­e­sis that as­sumes that hu­mans poss­es ge­net­i­cal­ly de­ter­mined in­nate lan­guage-​spe­cif­ic knowl­edge is ex­am­ined crit­i­cal­ly and al­ter­na­tive mod­els of lan­guage ac­qui­si­tion are dis­cussed. In ad­di­tion this chap­ter ad­dress­es is­sues that are dis­cussed con­tro­ver­sial­ly in cur­rent the­o­ry build­ing as for in­stance the ques­tion whether flat or bi­na­ry branch­ing struc­tures are more ap­pro­pri­ate, the ques­tion whether con­struc­tions should be treat­ed on the phrasal or the lex­i­cal level, and the ques­tion whether ab­stract, non-​vis­i­ble en­ti­ties should play a role in syn­tac­tic anal­y­ses. It is shown that the anal­y­ses that are sug­gest­ed in the re­spec­tive frame­works are often trans­lat­able into each other. The book clos­es with a sec­tion that shows how prop­er­ties that are com­mon to all lan­guages or to cer­tain lan­guage class­es can be cap­tured.

The webpage offers a download link for the current draft, teaching materials and a BibTeX file of all publications that the author cites in his works.

Interesting because of the application of these models to a language other than English and the author’s attempt to help readers avoid semantic confusion:

Unfortunately, linguistics is a scientific field which is afflicted by an unbelievable degree of terminological chaos. This is partly due to the fact that terminology originally defined for certain languages (e. g. Latin, English) was later simply adopted for the description of other languages as well. However, this is not always appropriate since languages differ from one another greatly and are constantly changing. Due to the problems this caused, the terminology started to be used differently or new terms were invented. when new terms are introduced in this book, I will always mention related terminology or differing uses of each term so that readers can relate this to other literature.

Unfortunately, it does not appear like the author gathered the new terms up into a table or list. Creating such a list from the book would be a very useful project.

FilterGraph

October 22nd, 2014

FilterGraph

From the wiki:

Filtergraph allows you to create interactive portals from datasets that you import. As a web application, no downloads are necessary – it runs and updates in real time on your browser as you make changes within the portal. All that you need to start a portal is an email address and a dataset in a supported type. Creating an account is completely free, and Filtergraph supports a wide variety of data types. For a list of supported data types see “ Supported File Types ”. (emphasis in original)

Just in case you are curious about the file types:

Filtergraph will allow you to upload dataset files in the following formats:

ASCII text Tab, comma and space separated
Microsoft Excel *.xls, *.xlsx
SQLite *.sqlite
VOTable *.vot, *.xml
FITS *.fits
IPAC *.tbl
Numpy *.npy
HDF5 *.h5

You can upload files up to 50MB in size. Larger files can be accommodated if you contact us via a Feedback Form.

For best results:

  • Make sure each row has the same number of columns. If a row has an incorrect number of columns, it will be ignored.
  • Place a header in the first row to name each column. If a header cannot be found, the column names will be assigned as Column1, Column2, etc.
  • If you include a header, make the name of each column unique. Otherwise, the duplicate names will be modified.
  • For ASCII files, you may optionally use the ‘#’ symbol to designate a header.

Here is an example of an intereactive graph for earthquakes at FilterGraph:

graph of earthquakes

You can share the results of analysis and allow others to change the analysis of large data sets, without sending the data.

From the homepage:

Developed by astronomers at Vanderbilt University, Filtergraph is used by over 200 people in 28 countries to empower large-scale projects such as the KELT-North and KELT-South ground-based telescopes, the Kepler, Spitzer and TESS space telescopes, and a soil collection project in Bangladesh.

Enjoy!

Dabiq, ISIS and Data Skepticism

October 22nd, 2014

If you are following the Middle East, no doubt you have heard that ISIS/ISIL publishes Dabiq, a magazine that promotes its views. It isn’t hard to find articles quoting from Dabiq, but I wanted to find copies of Dabiq itself.

Clarion Project (Secondary Source for Dabiq)

After a bit of searching, I found that the Clarion Project is posting every issue of Dabiq as it appears.

The hosting site, Clarion Project, is a well known anti-Muslim hate group. The founders of the Clarion Project just happened to be full time employees of Aish Hatorah, a pro-Israel organization.

Coverage of Dabiq by Mother Jones (who should know better), ISIS Magazine Promotes Slavery, Rape, and Murder of Civilians in God’s Name relies on The Clarion Project “reprint” of Dabiq.

Internet Archive (Secondary Source for Dabiq)

The Islamic State Al-Hayat Media Centre (HMC) presents Dabiq Issue #1 (July 5, 2014).

All the issues at the Internet Archive claim to be from: “The Islamic State Al-Hayat Media Centre (HMC). I say “claim to be from” because uploading to the Internet Archive only requires an account with a verified email address. Anyone could have uploaded the documents.

Robert Mackey writes for the New York Times: Islamic State Propagandists Boast of Sexual Enslavement of Women and Girls and references Dabiq. I asked Robert for his source for Dabiq and he responded that it was the Internet Archive version.

Wall Street Journal

In Why the Islamic State Represents a Dangerous Turn in the Terror Threat, Gerald F. Seib writes:

It isn’t necessary to guess at what ISIS is up to. It declares its aims, tactics and religious rationales boldly, in multiple languages, for all the world to see. If you want to know, simply call up the first two editions of the organization’s remarkably sophisticated magazine, Dabiq, published this summer and conveniently offered in English online.

Gerald implies, at least to me, that Dabiq has a “official” website where it appears in multiple languages. But if you read Gerald’s article, there is no link to such a website.

I wrote to Gerald today to ask what site he meant when referring to Dabiq. I have not heard back from Gerald as of posting but will insert his response when it arrives.

The Jamestown Foundation

The Jamestown Foundation website featured: Hot Issue: Dabiq: What Islamic State’s New Magazine Tells Us about Their Strategic Direction, Recruitment Patterns and Guerrilla Doctrine by Michael W. S. Ryan, saying:

On the first day of Ramadan (June 28), the Islamic State in Iraq and Syria (ISIS) declared itself the new Islamic State and the new Caliphate (Khilafah). For the occasion, Abu Bakr al-Baghdadi, calling himself Caliph Ibrahim, broke with his customary secrecy to give a surprise khutbah (sermon) in Mosul before being rushed back into hiding. Al-Baghdadi’s khutbah addressed what to expect from the Islamic State. The publication of the first issue of the Islamic State’s official magazine, Dabiq, went into further detail about the Islamic State’s strategic direction, recruitment methods, political-military strategy, tribal alliances and why Saudi Arabia’s concerns that the Kingdom may be the Islamic State’s next target are well-founded.

Which featured a thumbnail of the cover of the first issue of Dabiq, with the following legend:

Dabiq Magazine (Source: Twitter user @umOmar246)

Well, that’s a problem because the Twitter user “@umOmar246″ doesn’t exist.

Don’t take my word for it, go to Twitter, search for “umOmar246,” limit search results to people and you will see:

twitter results

I took the screen shot today just in case the results change at some point in time.

Other Media

Other media carry the same stories but without even attempting to cite a source. For example:

Jerusalem Post: ISIS threatens to conquer the Vatican, ‘break the crosses of the infidels’. Source? None.

Global News: The twisted view of ISIS in latest issue of propaganda magazine Dabiq by Nick Logan.

I don’t think that Nick appreciates the irony of the title of his post. Yes, this is a twisted view of ISIS. The question is who is responsible for it?

General Comments

Pick any issue of Dabiq and skim through it. What impressed me was the “over the top” presentation of cruelty. The hate literature I am familiar with (I grew up in the Deep South in the 1960’s) usually portrays the atrocities of others, not the group in question. Hate literature places its emphasis on the “other” group, the one to be targeted, not itself.

Analysis

First and foremost, the lack of any “official” site of origin for Dabiq makes me highly suspicious of the authenticity of the materials that claim to originate with ISIS.

Second, why would ISIS rely upon the Clarion Project as a distributor for its English language version of Dabiq, along with the Internet Archive?

Third, what are we to make of missing @umOmar246 from Twitter? Before you say that the account has closed, http://twittercounter.com/
doesn’t know that user either:

twitter counter results

A different aspect of consistency on distributed data. The aspect of getting “caught” because distributed data is difficult to make consistent.

Fourth, the media coverage examined relies upon sites with questionable authenticity but cites the material found there as though authoritative. Is this a new practice in journalism? Some of the media outlets examined are hardly new and upcoming Internet news sites.

Finally, the content of the magazines themselves don’t ring true for hate literature.

Conclusion

Debates about international and national policy should not be based on faked evidence (such as “yellow cake uranium“) or faked publications.

Based on what I have uncovered so far, attributing Dabiq to ISIS is highly questionable.

It appears to be an attempt to discredit ISIS and to provide a basis for whipping up support for military action by the United States and its allies.

The United States destroyed the legitimate government of Iraq on the basis of lies and fabrications. If only for nationalistic reasons, not spending American funds and lives based on a tissue of lies, let’s not make the same mistake again.

Disclaimer: I am not a supporter of ISIS nor would I choose to live in their state should they establish one. However, it will be their state and I lack the arrogance to demand that others follow social, religious or political norms that I prefer.

PS: If you have suggestions for other factors that either confirm a link between ISIS and Dabiq or cast further doubt on such a link, please post them in comments. Thanks!

Tweet NLP

October 21st, 2014

TWeet NLP (Carnegie Mellon)

From the webpage:

We provide a tokenizer, a part-of-speech tagger, hierarchical word clusters, and a dependency parser for tweets, along with annotated corpora and web-based annotation tools.

See the website for further details.

I can understand vendors mining tweets and try to react to every twitch in some social stream but the U.S. military is interested as well.

“Customer targeting” in their case has a whole different meaning.

Assuming you can identify one or more classes of tweets, would it be possible to mimic those patterns, albeit with some deviation in the content of the tweets? That is what tweet content is weighted heavier that other tweet content?

I first saw this in a tweet by Peter Skomoroch.

The Cartographer Who’s Transforming Map Design

October 21st, 2014

The Cartographer Who’s Transforming Map Design by Greg Miller.

From the post:

Cindy Brewer seemed to attract a small crowd everywhere she went at a recent cartography conference here. If she sat, students and colleagues milled around, waiting for a chance to talk to her. If she walked, a gaggle of people followed.

Brewer, who chairs the geography program at Penn State, is a popular figure in part because she has devoted much of her career to helping other people make better maps. By bringing research on visual perception to bear on design, Brewer says, cartographers can make maps that are more effective and more intuitive to understand. Many of the same lessons apply equally well to other types of data visualization.

Brewer’s best-known invention is a website called Color Brewer, which helps mapmakers pick a color scheme that’s well-suited for communicating the particular type of data they’re mapping. More recently she’s moved on to other cartographic design dilemmas, from picking fonts to deciding what features should change or disappear as the scale of a map changes (or zooms in and out, as non-cartographers would say). She’s currently helping the U.S. Geological Survey apply the lessons she’s learned from her research to redesign its huge collection of national topographic maps.

A must read if you want to improve the usefulness of your interfaces.

I say a “must read,” but this is just an overview of Cindy’s work.

A better starting place would be Cindy’s homepage at UPenn.

The Harvard Classics: Download All 51 Volumes as Free eBooks

October 21st, 2014

The Harvard Classics: Download All 51 Volumes as Free eBooks by Josh Jones.

From the post:

Every revolutionary age produces its own kind of nostalgia. Faced with the enormous social and economic upheavals at the nineteenth century’s end, learned Victorians like Walter Pater, John Ruskin, and Matthew Arnold looked to High Church models and played the bishops of Western culture, with a monkish devotion to preserving and transmitting old texts and traditions and turning back to simpler ways of life. It was in 1909, the nadir of this milieu, before the advent of modernism and world war, that The Harvard Classics took shape. Compiled by Harvard’s president Charles W. Eliot and called at first Dr. Eliot’s Five Foot Shelf, the compendium of literature, philosophy, and the sciences, writes Adam Kirsch in Harvard Magazine, served as a “monument from a more humane and confident time” (or so its upper classes believed), and a “time capsule…. In 50 volumes.”

What does the massive collection preserve? For one thing, writes Kirsch, it’s “a record of what President Eliot’s America, and his Harvard, thought best in their own heritage.” Eliot’s intentions for his work differed somewhat from those of his English peers. Rather than simply curating for posterity “the best that has been thought and said” (in the words of Matthew Arnold), Eliot meant his anthology as a “portable university”—a pragmatic set of tools, to be sure, and also, of course, a product. He suggested that the full set of texts might be divided into a set of six courses on such conservative themes as “The History of Civilization” and “Religion and Philosophy,” and yet, writes Kirsch, “in a more profound sense, the lesson taught by the Harvard Classics is ‘Progress.’” “Eliot’s [1910] introduction expresses complete faith in the ‘intermittent and irregular progress from barbarism to civilization.’”

Great reading in addition to being a snapshot of a time in history.

Good data set for testing text analysis tools.

For example, Josh mentions “progress” as a point of view in the Harvard Classics, as if that view does not persist today. I would be hard pressed to explain American foreign policy and its posturing about how states should behave aside from “complete faith” in progress.

What text collection would you compare the Harvard Classics to today to arrive at a judgement on their respective views of progress?

I first saw this in a tweet by Open Culture.

Top 30 Computer Science and Programming Blogs 2014

October 21st, 2014

Top 30 Computer Science and Programming Blogs 2014 by Benjamin Hicks.

From the post:

A major in computer science and programming opens many lucrative doors. Many of these students become software engineers and programmers at one of the many technological companies throughout the country. Others find computational theory attractive. Graduate degrees strengthen students’ skills, increasing their value to employers. Since the field is rapidly advancing, many blogs have sprung up to address all aspects of the field. But which ones are the best to read and why? This list of thirty blogs represents a diverse array of perspectives on computer science, programming, computational theory, and the intersection of computer science with contemporary issues such as education, women in the sciences, business, and many more.

Great material for a “seed list” if you are running your own search engine!

I first saw this in a tweet by Computer Science.

TinkerPop 3.0.0.M4 Released (A Gremlin Rāga in 7/16 Time)

October 21st, 2014

TinkerPop 3.0.0.M4 Released (A Gremlin Rāga in 7/16 Time) by Marko Rodriguez.

From the post:

TinkerPop (http://tinkerpop.com) is happy to announce the release of TinkerPop 3.0.0.M4.

gremlin-hindu

Documentation

User Documentation: http://www.tinkerpop.com/docs/3.0.0.M4/
Core JavaDoc: http://www.tinkerpop.com/javadocs/3.0.0.M4/core/ [user javadocs]
Full JavaDoc : http://www.tinkerpop.com/javadocs/3.0.0.M4/full/ [vendor javadocs]

Downloads

Gremlin Console: http://tinkerpop.com/downloads/3.0.0.M4/gremlin-console-3.0.0.M4.zip
Gremlin Server: http://tinkerpop.com/downloads/3.0.0.M4/gremlin-server-3.0.0.M4.zip

There were lots of updates in this release — with a lot of valuable feedback provided by Titan (Matthias), Titan-Hadoop (Dan), FoundationDB (Mike), PostgreSQL-Gremlin (Pieter), and Gremlin-Scala (Mike).

https://github.com/tinkerpop/tinkerpop3/blob/master/CHANGELOG.asciidoc

We are very close to a GA. We think that either there will be a “minor M5″ or the next release will be GA. Why the delay? We are currently working closely with the Titan team to see if there are any problems in our interfaces/test-suites/etc. The benefit of working with the Titan team is that they are doing both OLTP and OLAP so are covering the full gamut of the TinkerPop3 API. Of course, we have had lots of experience with these APIs for both Neo4j (OTLP) and Giraph (OLAP), but to see it standup to yet another vendor’s requirements will be a confidence boost for GA. If you are vendor, please feel free to join the conversation as your input is crucial to making sure GA meets everyone’s needs.

A few important notes for users:
1. The GremlinKryo serialization format is not guaranteed to be stable from MX to MY. By GA it will be locked.
2. Neo4j-Gremlin’s disk representation is not guaranteed to be stable from MX to MY. By GA it will be locked.
3. Giraph-Gremlin’s Hadoop Writable specification is not guaranteed to be stable from MX to MY. By GA it will be locked.
4. VertexProgram, Memory, Step, SideEffects, etc. hidden and system labels may change between MX and MY. By GA they will be locked.
5. Package and class names might change from MX to MY. By GA they will be locked.

Thank you everyone. Please play and provide feedback. This is the time to get your ideas into TinkerPop3 as once it goes GA, sweeping changes are going to be more difficult.

How to Make More Published Research True

October 21st, 2014

How to Make More Published Research True by John P. A. Ioannidis. (DOI: 10.1371/journal.pmed.1001747)

If you think the title is provocative, check out the first paragraph:

The achievements of scientific research are amazing. Science has grown from the occupation of a few dilettanti into a vibrant global industry with more than 15,000,000 people authoring more than 25,000,000 scientific papers in 1996–2011 alone [1]. However, true and readily applicable major discoveries are far fewer. Many new proposed associations and/or effects are false or grossly exaggerated [2],[3], and translation of knowledge into useful applications is often slow and potentially inefficient [4]. Given the abundance of data, research on research (i.e., meta-research) can derive empirical estimates of the prevalence of risk factors for high false-positive rates (underpowered studies; small effect sizes; low pre-study odds; flexibility in designs, definitions, outcomes, analyses; biases and conflicts of interest; bandwagon patterns; and lack of collaboration) [3]. Currently, an estimated 85% of research resources are wasted [5]. (footnote links omitted, emphasis added)

I doubt anyone can disagree with the need for reform in scientific research, but it is one thing to call for reform in general versus the specific.

The following story depends a great deal on cultural context, Southern religious cultural context, but I will tell the story and then attempt to explain if necessary.

One Sunday morning service the minister was delivering a powerful sermon on sins that his flock could avoid. He touched on drinking and smoking at length and as he ended each of those, an older woman in the front pew would “Amen!” very loudly. The same response was given to his condemnation of smoking. Finally, the sermon touched on dipping snuff and chewing tobacco. Dead silence from the older woman on the front row. The sermon ended some time later, hymns were sung and the congregation was dismissed.

As the congregation exited the church, the minister stood at the door, greeting one and all. Finally the older woman from the front pew appeared and the minister greeted her warmly. She had after all, appeared to enjoy most of his sermon. After some small talk, the minister did say: “You liked most of my sermon but you became very quite when I mentioned dipping snuff and chewing tobacco. If you don’t mind, can you tell me what was different about that part?” To which the old woman replied: “I was very happy while you were preaching but then you went to meddling.”

So long as the minister was talking about the “sins” that she did not practice, that was preaching. When the minister starting talking about “sins” she committed like dipping snuff or chewing tobacco, that was “meddling.”

I suspect that Ioannidis’ preaching will find widespread support but when you get down to actual projects and experiments, well, you have gone to “meddling.”

In order to root out waste, it will be necessary to map out who benefits from such projects, who supported them, who participated, and their relationships to others and other projects.

Considering that universities are rumored to get at least fifty (50) to (60) percent of grants as administrative overhead, they are unlikely to be your allies in creating such mappings or reducing waste in any way. Appeals to funders may be effective, save some funders, like the NIH, have an investment in the research structure as it exists.

Whatever the odds of change, naming names, charting relationships over time and interests in projects is at least a step down the road to useful rather than remunerative scientific research.

Topic map excel at modeling relationships, whether known at the outset of your tracking or lately discovered, unexpectedly.

PS: With a topic map you can skip endless committee meetings with each project to agree on how to track that project and their methodologies for waste, should any waste exists. Yes, the first line of a tar baby (in it’s traditional West African sense) defense by universities and others, let’s have a pre-meeting to plan our first meeting, etc.

Big Data: 20 Free Big Data Sources Everyone Should Know

October 21st, 2014

Big Data: 20 Free Big Data Sources Everyone Should Know Bernard Marr.

From the post:

I always make the point that data is everywhere – and that a lot of it is free. Companies don’t necessarily have to build their own massive data repositories before starting with big data analytics. The moves by companies and governments to put large amounts of information into the public domain have made large volumes of data accessible to everyone.

Any company, from big blue chip corporations to the tiniest start-up can now leverage more data than ever before. Many of my clients ask me for the top data sources they could use in their big data endeavour and here’s my rundown of some of the best free big data sources available today.

I didn’t see anything startling but it is a good top 20 list for a starting point. Would make a great start on a one to two page big data cheat sheet. Will have to give some thought to that idea.

LSD Dimensions

October 20th, 2014

LSD Dimensions

From the about page: http://lsd-dimensions.org/dimensions

LSD Dimensions is an observatory of the current usage of dimensions and codes in Linked Statistical Data (LSD).

LSD Dimensions is an aggregator of all qb:DimensionProperty resources (and their associated triples), as defined in the RDF Data Cube vocabulary (W3C recommendation for publishing statistical data on the Web), that can be currently found in the Linked Data Cloud (read: the SPARQL endpoints in Datahub.io). Its purpose is to improve the reusability of statistical dimensions, codes and concept schemes in the Web of Data, providing an interface for users (future work: also for programs) to search for resources commonly used to describe open statistical datasets.

Usage

The main view shows the count of queried SPARQL endpoints and the number of retrieved dimensions, together with a table that displays these dimensions.

  • Sorting. Dimensions can be sorted by their dimension URI, label and number of references (i.e. number of times a dimension is used in the endpoints) by clicking on the column headers.
  • Pagination. The number of rows per page can be customized and browsed by clicking at the bottom selectors.
  • Search. String-based search can be performed by writing the search query in the top search field.

Any of these dimensions can be further explored by clicking at the eye icon on the left. The dimension detail view shows

  • Endpoints.. The endpoints that make use of that dimension.
  • Codes. Popular codes that are defined (future work: also assigned) as valid values for that dimension.

Motivation

RDF Data Cube (QB) has boosted the publication of Linked Statistical Data (LSD) as Linked Open Data (LOD) by providing a means “to publish multi-dimensional data, such as statistics, on the web in such a way that they can be linked to related data sets and concepts”. QB defines cubes as sets of observations affected by dimensions, measures and attributes. For example, the observation “the measured life expectancy of males in Newport in the period 2004-2006 is 76.7 years” has three dimensions (time period, with value 2004-2006; region, with value Newport; and sex, with value male), a measure (population life expectancy) and two attributes (the units of measure, years; and the metadata status, measured, to make explicit that the observation was measured instead of, for instance, estimated or interpolated). In some cases, it is useful to also define codes, a closed set of values taken by a dimension (e.g. sensible codes for the dimension sex could be male and female).

There is a vast diversity of domains to publish LSD about, and quite some dimensions and codes can be very heterogeneous, domain specific and hardly comparable. To this end, QB allows users to mint their own URIs to create arbitrary dimensions and associated codes. Conversely, some other dimensions and codes are quite common in statistics, and could be easily reused. However, publishers of LSD have no means to monitor the dimensions and codes currently used in other datasets published in QB as LOD, and consequently they cannot (a) link to them; nor (b) reuse them.

This is the motivation behind LSD Dimensions: it monitors the usage of existing dimensions and codes in LSD. It allows users to browse, search and gain insight into these dimensions and codes. We depict the diversity of statistical variables in LOD, improving their reusability.

(Emphasis added.)

The highlighted text:

There is a vast diversity of domains to publish LSD about, and quite some dimensions and codes can be very heterogeneous, domain specific and hardly comparable.

is the key isn’t it? If you can’t rely on data titles, users must examine the data and determine which sets can or should be compared.

The question then is how do you capture the information such users developed in making those decisions and pass it on to following users? Or do you just allow following users make their own way afresh?

If you document the additional information for each data set, by using a topic map, each use of this resource becomes richer for the following users. Richer or stays the same. Your call.

I first saw this in a tweet by Bob DuCharme. Who remarked this organization has a great title!

If you have made it this far, you realize that with all the data set, RDF and statistical language this isn’t the post you were looking for. ;-)

PS: Yes Bob, it is a great title!