Garbage collection tips in Java

The OverOps blog has a nice overview of Java garbage collection followed by some tips, including writing GC efficient code.

https://blog.takipi.com/improve-your-application-performance-with-garbage-collection-optimization/

Dictionary of Algorithms and Data Structures

The National Institute of Standards and Technology hosts a Dictionary of Algorithms and Data Structures on its website.   This is a great resource for students as well as experienced professionals who want to refresh their memory about the exact definition of a red-black tree.

The dictionary is edited and maintained by Paul Black and Vreda Pieterse.  It's quite extensive, with references, bibliography, and a guide for how to cite.

The URL is https://xlinux.nist.gov/dads/ .

National Consortium for Data Science "DataBytes" Webinars

Check out the National Consortium for Data Science "DataBytes" Webinars, available at:

http://data2discovery.org/databytes-webinars/

The webinars are held on the first Wednesday of every month, excluding July.

According to the NCDS site,  DataBytes is "an NCDS-sponsored lunchtime webinar series that gives our members and the larger data community the chance to discuss the most pressing and interesting issues in data science. "

About APIs

A recent article in JavaWorld talks about the wide variety of APIs now available for use.  While the concept of APIs is not new, recent "advances in infrastructure, type and variety of programming languages, and abundant computing resources have created new opportunities. "

The post links out to several other interesting article about the challenges and opportunities of API development.

View the article here:  http://www.javaworld.com/article/2899704/enterprise-application-integration/all-eyes-on-the-api.html

Watson Analytics Tool

IBM has released a new tool for performing analytics on big data.   The name of the tool is "Watson Analytics," and the name refers to Watson, the famous IBM computer that won on Jeopardy. Essentially this tool allows users to pose natural language questions and get answers derived from large data sets.  An example given in the New York Times article linked below is "What high-value customers am I most likely to close sales with in the next 30 days?"     The idea is that a business user can use the tool without having to talk to a data scientist first.  The author of the New York times article ponders whether this is the "killer app" for Big Data.

Here is the link:

http://nyti.ms/1qbtCnt

New ways of representing data

The New York Times Bits blog this week has an article titled "A Makeover for Maps" that talks about cutting edge visualizations of data.  The article cites some interesting new depictions of data--such as the one created by Eric Rodendeck of  Stamen Design that creates  "...a representation of how photos spread on Facebook that looks like ice crystals forming on a car window."

The article goes on to talk about the idea of creating visualizations that can be used by different audiences -- like a dynamic chart of Nasdaq financial data that can be read one way by a pension fun manager looking for trends and another way by an SEC investigator looking into an unusual spike in trading during a given day.

Check out the full article with multiple visualization examples at

http://bits.blogs.nytimes.com/2014/01/06/a-makeover-for-maps/?smid=pl-share

Looking at the problems on the healthcare.gov site

You may have heard the news about people in the U.S. experiencing problems trying to sign up for healthcare on the new healthcare.gov site.  This site is perhaps the most visible manifestation of the Affordable Care Act, aka "Obamacare."  On healthcare.gov, users can search the "Health Insurance Marketplace" for coverage.  However, the site has been plagued by reports of slowness and poor user experience. 

Several online articles attempt to surmise what is going wrong with the site from a technical perspective.  Regardless of your personal views on the Affordable Care Act, I think these are relevant for us to read because they provide us a chance to avoid making the same (apparent) technical mistakes in our projects.  I'd like to highlight some general points that I have read so far:

• Load test both the interface _and_ the back end functionality. 
• User experience (UX) should not be an afterthought
• Poor validation logic (eg requiring numbers as part of username/password but not informing user of this beforehand or in error messages) leads to extremely poor UX

For more on the technical issues with healthcare.gov, see the following articles:

Why HealthCare.gov is still a mess - JavaWorld

What Developers Can Learn from healthcare.gov - Programming - O'Reilly Media

You Can’t Legislate Away the Time, Money and Features Law - Programming - O'Reilly Media

And here is a contrarian opinion, stating that the architecture of healthcare.gov is sound, but that the project was rushed:

Why The Experts Are Probably Wrong About The Healthcare.gov Crack-Up | John Pavley