T-Shirt search engines tag and help you find shirt designs

http://www.techcrunch.com/2008/09/01/the-vaynerchucks-launch-t-shirt-search-engine-pleasedressme/

Sounds silly, right?  Why would you need an entire search engine devoted to t-shirts?  However, clothing falls into that category of items that are plentiful and searchable online, yet difficult to search on for meaningful (visual) characteristics.

This new search engine, PleaseDressMe, was recently launched.  It searches some of the top t-shirt websites and tags them with useful, more general or esoteric keywords, such as “sarcasm”, “politics”, or “typography”.  Clothing is a good example of something that is easy to find when you’re not looking for it, but much more difficult to search precisely on concepts, or characteristics like fabric or sleeve length.  This search engine aims to make it easier for people to find comprehensive results of the kinds of t-shirts they’re looking for without having to visit sites individually or wade through pages of non-product google search results.

The TechCrunch article also has several comments pointing to Teenormous, a similar search engine with many more shirts indexed.

Lecture: CONTROLLED NAMES AND VOCABULARIES

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Rating the Ratings

Rating the ratings by Stephen Whitty/The Star-Ledger

http://www.nj.com/entertainment/tv/index.ssf/2008/08/post_3.html

The way in which feature films are given MPAA ratings often appears haphazard, governed more by studio muscles and politics rather than a reasonable set of guidelines. 

The highly political nature of this governing body was featured in Kirby Dick’s Documentary This Film Is Not Yet Rated, which came out in 2006. 

Most recently, controversy buzzed about regarding Kevin’s Smith’s latest film entitled Zack and Miri Make a Porno, starring Seth Rogan and Elizabeth Banks, when it was slapped with an NC-17 rating—which was overturned by the appeals board and given a box-office friendlier R rating. 

The article talks bout the current flawed system and lists examples of movies and their ratings to delineate the inconsistency within the system.  The author ponders what and who governs these guidelines and laments the politics that clearly influence and dictate the direction of the board. 

Categorizing a subjective and nuanced product, something that is difficult to be scientifically calibrated seems to be a consistent challenge not only for movie ratings but also in a variety of fields beyond the arts.  A product that is measured/judged on opinion is naturally subject to subjectivity.  Finding the best method of categorization will be an interesting and relevant challenge.  

3 – Organization {and, of, vs} Retrieval

5 – Concepts and Categories

7 – Controlled Names and Vocabularies

8 – Classification

12 – Enterprise/Institutional Categorization & Standards 

 

 

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Data Fusion: The Ups and Downs of All-Encompassing Digital Profiles

The current issue of the magazine Scientific American includes several articles on the rise of digital information and its use, from RFID and biometrics to eavesdropping. I found an article on data fusion, aka data integration, as the most relevant for this discussion.

Data Fusion: The Ups and Downs of All-Encompass Digital Profiles

The article begins with the author’s reflection on his experience traveling internationally several years ago, when his credit card issuer blacklisted his card because the company’s anti-fraud data mining algorithm detected potential fraud. He had merely bought a latte and a cell phone SIM in England. The company knew he was in England, as he’d bought his ticket to England with the same credit card! Shouldn’t they have known it was him?

From this introduction the author traces the history of data mining efforts in the United States, focusing in particular on the challenges of integrating multiple data sources together in order to data mine effectively. He cites examples from DARPA’s counter-terrorism efforts and the Department of Health & Human Services anti-fraud efforts, exploring how data mining is viewed as an ultimate tool, however one that is still rife with inconsistency and errors.

Errors primarily arise from the difficulty normalizing data from varying sources with varying levels of detail and uncertainty. And perhaps most importantly, that once those data sources are aligned, how does one guarantee identity? Who’s who? Am I Andy Brooks, A.L Brooks, and/or Andrew Brooks?

After examining data mining’s shortfalls, the author turns to examples of more effective work done with data fusion and data mining. The winners? Casinos! Think of those wallet-sized perks cards casinos are so happy to give you. In order to counter the efforts of cheaters, casinos have long funded development of non-obvious relationship analysis techniques. The techniques attempt to normalize data across multiple sources in an evolving way, one that tolerates error and uncertainty, and strives to grow more intelligent over time.

The article concludes with the author’s most important point – that similar to the history of cryptography, the public is essentially left out of any discussions about the use of data mining and data fusion. We don’t really know when it’s used, by whom, and for what purpose. We often only know after the fact, such as when the author’s credit card was declined when trying to buy a train ticket in London.

Candidate Lectures:

11. Information Integration & Interoperability (10/6)

15. Personal Information Management (10/20)

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Redefining the Kilogram

http://www.azom.com/News.asp?NewsID=13480

Who knew that the kilogram was defined by a piece of platinum-iridium stored in a vault in France? Well, not me. I just took for granted that a kilogram was 1kg on a scale and never thought about the core of the definition. Apparently “Le Grand K” has been losing weight and now scientists are looking for a more constant and accurate way to define the kilogram, such as counting the number of atoms in a silicon crystal (duh). They claim that the kilogram as we know it will remain the same (don’t panic!), it is just the definition that is changing to ensure more accuracy.

Relevant Lectures: CONCEPTS & CATEGORIES (9/15); CONTROLLED NAMES AND VOCABULARIES (9/22); ENTERPRISE / INSTITUTIONAL CATEGORIZATION & STANDARDS (10/8)

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