What Makes a Tech Center?

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This morning’s local paper featured an article about Control Data Corporation, a major player in the olden days of mainframe computing.

By the late 1970s, [Control Data] had made the Twin Cities one of five U.S.
computer industry centers (a distinction that is now only a memory). By
encouraging entrepreneurship among employees, it spawned dozens of
local spinoff companies, including the supercomputer firm Cray Research
(also now gone). At its peak, CDC had 60,000 employees and about $5
billion in revenue.

This summer, I went and worked in Silicon Valley, to see what a modern day computer industry center was like.  It was indeed an exciting environment, full of new companies, people with ideas, and support for those ideas.  Contrast that with Minneapolis (a city I very much love), where technology innovation feels particularly limited to a few industries.  And yet, Minneapolis/St. Paul ranks as the #1 best metro center for business.  Where are the tech startups?

It feels as though Minneapolis is prime for a computing technology resurgence.  But I’m not sure what the catalyst of that resurgence will be, or when it will happen.

Max

How much does Shilad love presenting his research?

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Shilad Sen is very excited about his poster This much!

 

Max Harper and his poster Reid Priedhorsky

Max and Reid choose to show their love of research in much calmer ways.

The Computer Science department hosted their biennial open house last week. The morning program included a poster session, populated with current graduate students and their research. Several GroupLens students presented some of their current research.

Shilad Sen – Better Tagging Systems

Max Harper – Predictors of Answer Quality in Online Q&A Sites

Reid Priedhorsky – Creating, Destroying and Restoring Value in Wikipedia

Nishi Kapoor (not pictured) – TechLens: A Researcher’s Desktop

Sara Drenner (not pictured) – Barriers to Entry in Recommender Systems

 

today’s xkcd

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Yesterday’s xkcd comic:

I found this highly amusing, particularly in light of (a) knowing that applications I use frequently are full of SQL injection bugs and have been for years despite my complaints, and (b) as a programmer, observing how easy it is to skip input sanitization.

Microtrends and collaborative filtering?

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I’ve recently been hearing a bit about Mark Penn’s book "Microtrends: The Small Froces Behind Tomorrow’s Big Changes". As this review says, Penn analyzes poll and survey data to identify 75 important microtrends (which appear to correspond to ‘small’ segments of the US population, say at least 3 million) that, he believes, are interesting and important.

How, since Mark Penn is the guy who identified the ‘soccer mom’ demographic for Bill Clinton’s 1996 re-election and is now the chief political advisor to Hillary Clinton, when he talks, people listen.

And when I listend, I’ve found what he has to say interesting. However, since I haven’t read the book, I don’t know exactly how he comes up with his microtrends. It seems like the scientific approach would be to apply clustering algorithms or factor analysis or some such technique, which, as far as I can tell from browsing reviews.

I wonder what such an approach would reveal: if you ran a clustering algorithm, say, on a large survey dataset, would the clusters include Penn’s microtrends? Would one even be able to make sense of the clusters?

How do software patterns evolve?

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I was talking yesterday afternoon with several other lab members about Martin Fowler’s "Patterns of Enterprise Application Architecture." In his book, Fowler admits that most patterns aren’t anything new. "Creating" a software pattern is just naming and describing a software practice already used by some developers. Of course, Fowler presents patterns with amazing clarity and skill, so his contributions are valuable to the development community.

Since our research group is interested in social communities, we were curious about how new software practices evolve into well-known design patterns. Do most patterns start with a bang from a few highly influential, outspoken developers? Is there gradually increased adoption until the pattern reaches a critical mass of people? Another possibility is that as the global software environment changes, many groups of people stumble onto the same pattern at the same time.

What’s your opinion?