ashley

Part of the problem is identifying the actual abusers of the visa-system applicable to skilled workers, i.e. what makes someone "skilled" with "specialized knowledge". The public debate in elections may make the biggest noise about undocumented immigrants, but there is behind-the-scenes discussion about the H1-B and L-1 system. This August, the USCIS will announce its decision on expanding the L-1 definition of "specialized knowledge". The L-1 class, the lesser-known brother of the H1-B, is a 3-year non-immigrant visa that is extendable for up to 5 years (7 years for the L-1A) and can be reissued if the employee in question leaves the US and works for the sponsoring company for another year. People are eligible to apply for the L-1 if they have worked for a US company abroad or will be opening and running US-branch office for a foreign company (other cases apply, just not available off the top of my head. The L-1A is for execs and managers, the L1-B for specialized knowledge workers). No degree or education requirement is necessary for the L-1B by the way. No quota either, like with the H-1B. About a third of applications are rejected though, biased towards India. Abusers of the H1-B and L-1 system are the blue-chip, non-tech companies who use out-sourcing IT companies like Cognizant, Infosys, Tata, Wipro to cut costs in their tech departments. Talking about immigration reform is not so much about lack of incentive as it is about the emotions arising from the stories of Disney IT workers being fired and having to retrain their H1-B replacements who are getting paid lower wages. Another aspect not discussed is whether expanding the H1-B system perpetuates ageism in the Valley -- why not push out older workers at the expense of getting fresh foreign grads? No large change can happen when fear is present.

Ah, somehow I skipped your comment when reading through them; otherwise I would've just upvoted instead of posted :)

>>I'm thinking now either an external hard drive, or on a VPS I can just tunnel into. Consider setting up an ElasticSearch cluster somewhere, like with AWS, which takes plugins for ElasticSearch. Once you've indexed your data with ES, then queries are pretty easy (JSON-based). This would also solve your other problem with data visualization. ElasticSearch has an analytics tool called Kibana. Pretty useful and doesn't require too much effort to set up or use. I'm using this setup for a sentiment analysis project myself.

You didn't mention the libraries in your NLP pipeline (guessing NLTK bc of the Python?), but if you're doing LSM compatibility, I'm guessing you might be interested in clustering or topic-modelling algorithms and such...Mahout integrates easily with ElasticSearch.